

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						lowerCAmelCase							:  Optional[Any]   	=						"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	):
						if attention_mask is None:
												SCREAMING_SNAKE_CASE_: Dict  		=       np.where(input_ids != config.pad_token_id				,	1				,	0    )
						if decoder_attention_mask is None:
												SCREAMING_SNAKE_CASE_: int  		=       np.where(decoder_input_ids != config.pad_token_id				,	1				,	0    )
						if head_mask is None:
												SCREAMING_SNAKE_CASE_: str  		=       np.ones((config.encoder_layers, config.encoder_attention_heads)    )
						if decoder_head_mask is None:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.ones((config.decoder_layers, config.decoder_attention_heads)    )
						if cross_attn_head_mask is None:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.ones((config.decoder_layers, config.decoder_attention_heads)    )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     int   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Optional[int]=13   ,						lowerCAmelCase__  :     Optional[Any]=7   ,						lowerCAmelCase__  :     Optional[int]=True   ,						lowerCAmelCase__  :     int=False   ,						lowerCAmelCase__  :     str=99   ,						lowerCAmelCase__  :     List[str]=16   ,						lowerCAmelCase__  :     List[Any]=2   ,						lowerCAmelCase__  :     Any=4   ,						lowerCAmelCase__  :     Dict=4   ,						lowerCAmelCase__  :     Optional[int]="gelu"   ,						lowerCAmelCase__  :     Optional[Any]=0.1   ,						lowerCAmelCase__  :     List[Any]=0.1   ,						lowerCAmelCase__  :     Any=32   ,						lowerCAmelCase__  :     str=2   ,						lowerCAmelCase__  :     str=1   ,						lowerCAmelCase__  :     Dict=0   ,						lowerCAmelCase__  :     Optional[int]=0.02   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       parent
									SCREAMING_SNAKE_CASE_: List[str]  		=       batch_size
									SCREAMING_SNAKE_CASE_: str  		=       seq_length
									SCREAMING_SNAKE_CASE_: int  		=       is_training
									SCREAMING_SNAKE_CASE_: List[str]  		=       use_labels
									SCREAMING_SNAKE_CASE_: List[Any]  		=       vocab_size
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_size
									SCREAMING_SNAKE_CASE_: str  		=       num_hidden_layers
									SCREAMING_SNAKE_CASE_: Tuple  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: List[str]  		=       intermediate_size
									SCREAMING_SNAKE_CASE_: int  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Dict  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_position_embeddings
									SCREAMING_SNAKE_CASE_: str  		=       eos_token_id
									SCREAMING_SNAKE_CASE_: List[str]  		=       pad_token_id
									SCREAMING_SNAKE_CASE_: List[str]  		=       bos_token_id
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       initializer_range



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size)   ,						3   ,						self.vocab_size)
									SCREAMING_SNAKE_CASE_: Any  		=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,						dtype=np.intaa))   ,						-1)

									SCREAMING_SNAKE_CASE_: str  		=       shift_tokens_right(lowerCAmelCase__   ,						1   ,						2)

									SCREAMING_SNAKE_CASE_: int  		=       BlenderbotSmallConfig(
									    vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						encoder_layers=self.num_hidden_layers   ,						decoder_layers=self.num_hidden_layers   ,						encoder_attention_heads=self.num_attention_heads   ,						decoder_attention_heads=self.num_attention_heads   ,						encoder_ffn_dim=self.intermediate_size   ,						decoder_ffn_dim=self.intermediate_size   ,						dropout=self.hidden_dropout_prob   ,						attention_dropout=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						pad_token_id=self.pad_token_id   ,						initializer_range=self.initializer_range   ,						use_cache=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       prepare_blenderbot_inputs_dict(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									return config, inputs_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.prepare_config_and_inputs()
									return config, inputs_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       20
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       model.encode(inputs_dict["input_ids"])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.init_cache(decoder_input_ids.shape[0]   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,						dtype="i4")

									SCREAMING_SNAKE_CASE_: Any  		=       jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]   ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,						)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.decode(
									    decoder_input_ids[:, :-1]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype="i4")
									SCREAMING_SNAKE_CASE_: List[str]  		=       model.decode(
									    decoder_input_ids[:, -1:]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Tuple  		=       model.decode(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3   ,						msg=F"Max diff is {diff}")




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: List[str]  		=       20
									SCREAMING_SNAKE_CASE_: int  		=       model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       model.encode(inputs_dict["input_ids"])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
									    ]   ,						axis=-1   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       model.init_cache(decoder_input_ids.shape[0]   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]   ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,						)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       model.decode(
									    decoder_input_ids[:, :-1]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype="i4")
									SCREAMING_SNAKE_CASE_: str  		=       model.decode(
									    decoder_input_ids[:, -1:]   ,						lowerCAmelCase__   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_attention_mask=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: List[str]  		=       model.decode(lowerCAmelCase__   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3   ,						msg=F"Max diff is {diff}")







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     99



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]   ,						dtype=np.intaa   ,						)

									SCREAMING_SNAKE_CASE_: Tuple  		=       input_ids.shape[0]
									SCREAMING_SNAKE_CASE_: List[str]  		=       BlenderbotSmallConfig(
									    vocab_size=self.vocab_size   ,						d_model=24   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=32   ,						decoder_ffn_dim=32   ,						max_position_embeddings=48   ,						eos_token_id=2   ,						pad_token_id=1   ,						bos_token_id=0   ,						)
									return config, input_ids, batch_size



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_config_and_data()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       lm_model(input_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["logits"].shape   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       BlenderbotSmallConfig(
									    vocab_size=self.vocab_size   ,						d_model=14   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=8   ,						decoder_ffn_dim=8   ,						max_position_embeddings=48   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,						dtype=np.intaa)
									SCREAMING_SNAKE_CASE_: int  		=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,						dtype=np.intaa)
									SCREAMING_SNAKE_CASE_: str  		=       lm_model(input_ids=lowerCAmelCase__   ,						decoder_input_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["logits"].shape   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,						dtype=np.intaa)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       shift_tokens_right(lowerCAmelCase__   ,						1   ,						2)
									SCREAMING_SNAKE_CASE_: int  		=       np.equal(lowerCAmelCase__   ,						1).astype(np.floataa).sum()
									SCREAMING_SNAKE_CASE_: Dict  		=       np.equal(lowerCAmelCase__   ,						1).astype(np.floataa).sum()
									self.assertEqual(shifted.shape   ,						input_ids.shape)
									self.assertEqual(lowerCAmelCase__   ,						n_pad_before - 1)
									self.assertTrue(np.equal(shifted[:, 0]   ,						2).all())







@require_flax
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase		,       UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     True
			_UpperCAmelCase   :							str										=     (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCAmelCase   :							Optional[Any]										=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxBlenderbotSmallModelTester(self)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

																					@jax.jit
																					def encode_jitted(lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple=None   ,						**lowerCAmelCase__  :     List[str]):
																											return model.encode(input_ids=lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__)

																					with self.subTest("JIT Enabled"):
																											SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       encode_jitted(**lowerCAmelCase__).to_tuple()

																					with self.subTest("JIT Disabled"):
																											with jax.disable_jit():
																																	SCREAMING_SNAKE_CASE_: str  		=       encode_jitted(**lowerCAmelCase__).to_tuple()

																					self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
																					for jitted_output, output in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
																											self.assertEqual(jitted_output.shape   ,						output.shape)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.encode(inputs_dict["input_ids"]   ,						inputs_dict["attention_mask"])

																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {
																					    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																					    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																					    "encoder_outputs": encoder_outputs,
																					}

																					@jax.jit
																					def decode_jitted(lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Any):
																											return model.decode(
																											    decoder_input_ids=lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						encoder_outputs=lowerCAmelCase__   ,						)

																					with self.subTest("JIT Enabled"):
																											SCREAMING_SNAKE_CASE_: Optional[Any]  		=       decode_jitted(**lowerCAmelCase__).to_tuple()

																					with self.subTest("JIT Disabled"):
																											with jax.disable_jit():
																																	SCREAMING_SNAKE_CASE_: List[Any]  		=       decode_jitted(**lowerCAmelCase__).to_tuple()

																					self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
																					for jitted_output, output in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
																											self.assertEqual(jitted_output.shape   ,						output.shape)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class_name.from_pretrained("facebook/blenderbot_small-90M")
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.ones((1, 1)) * model.config.eos_token_id
															SCREAMING_SNAKE_CASE_: Any  		=       model(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)









from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowerCAmelCase							:  int   	=						logging.get_logger(__name__)


# General docstring
lowerCAmelCase							:  int   	=						"""MobileNetV1Config"""

# Base docstring
lowerCAmelCase							:  List[Any]   	=						"""google/mobilenet_v1_1.0_224"""
lowerCAmelCase							:  Dict   	=						[1, 1024, 7, 7]

# Image classification docstring
lowerCAmelCase							:  Union[str, Any]   	=						"""google/mobilenet_v1_1.0_224"""
lowerCAmelCase							:  Any   	=						"""tabby, tabby cat"""


lowerCAmelCase							:  List[Any]   	=						[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       {}

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Any  		=       model.mobilenet_va
						else:
												SCREAMING_SNAKE_CASE_: int  		=       model

						SCREAMING_SNAKE_CASE_: Dict  		=       "MobilenetV1/Conv2d_0/"
						SCREAMING_SNAKE_CASE_: str  		=       backbone.conv_stem.convolution.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.bias
						SCREAMING_SNAKE_CASE_: int  		=       backbone.conv_stem.normalization.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.running_mean
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       backbone.conv_stem.normalization.running_var

						for i in range(13    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       i + 1
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       i * 2

												SCREAMING_SNAKE_CASE_: Any  		=       backbone.layer[pt_index]
												SCREAMING_SNAKE_CASE_: Any  		=       f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: str  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_var

												SCREAMING_SNAKE_CASE_: Tuple  		=       backbone.layer[pt_index + 1]
												SCREAMING_SNAKE_CASE_: List[str]  		=       f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
												SCREAMING_SNAKE_CASE_: int  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_var

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       "MobilenetV1/Logits/Conv2d_1c_1x1/"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.classifier.weight
												SCREAMING_SNAKE_CASE_: Tuple  		=       model.classifier.bias

						return tf_to_pt_map







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						try:
												import numpy as np
												import tensorflow as tf
						except ImportError:
												logger.error(
												    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
												    "https://www.tensorflow.org/install/ for installation instructions."    )
												raise

						# Load weights from TF model
						SCREAMING_SNAKE_CASE_: int  		=       tf.train.list_variables(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       {}
						for name, shape in init_vars:
												logger.info(f"Loading TF weight {name} with shape {shape}"    )
												SCREAMING_SNAKE_CASE_: Any  		=       tf.train.load_variable(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       array

						# Build TF to PyTorch weights loading map
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       _build_tf_to_pytorch_map(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						for name, pointer in tf_to_pt_map.items():
												logger.info(f"Importing {name}"    )
												if name not in tf_weights:
																		logger.info(f"{name} not in tf pre-trained weights, skipping"    )
																		continue

												SCREAMING_SNAKE_CASE_: int  		=       tf_weights[name]

												if "depthwise_weights" in name:
																		logger.info("Transposing depthwise"    )
																		SCREAMING_SNAKE_CASE_: int  		=       np.transpose(_UpperCAmelCase				,	(2, 3, 0, 1)    )
												elif "weights" in name:
																		logger.info("Transposing"    )
																		if len(pointer.shape    ) == 2:  # copying into linear layer
																								SCREAMING_SNAKE_CASE_: List[str]  		=       array.squeeze().transpose()
																		else:
																								SCREAMING_SNAKE_CASE_: Any  		=       np.transpose(_UpperCAmelCase				,	(3, 2, 0, 1)    )

												if pointer.shape != array.shape:
																		raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"    )

												logger.info(f"Initialize PyTorch weight {name} {array.shape}"    )
												SCREAMING_SNAKE_CASE_: int  		=       torch.from_numpy(_UpperCAmelCase    )

												tf_weights.pop(_UpperCAmelCase				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/RMSProp"				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/RMSProp_1"				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/ExponentialMovingAverage"				,	_UpperCAmelCase    )

						logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()    )}"    )
						return model







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       features.shape[-2:]
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       conv_layer.stride
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       conv_layer.kernel_size

						if in_height % stride_height == 0:
												SCREAMING_SNAKE_CASE_: int  		=       max(kernel_height - stride_height				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       max(kernel_height - (in_height % stride_height)				,	0    )

						if in_width % stride_width == 0:
												SCREAMING_SNAKE_CASE_: str  		=       max(kernel_width - stride_width				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Dict  		=       max(kernel_width - (in_width % stride_width)				,	0    )

						SCREAMING_SNAKE_CASE_: str  		=       pad_along_width // 2
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pad_along_width - pad_left
						SCREAMING_SNAKE_CASE_: int  		=       pad_along_height // 2
						SCREAMING_SNAKE_CASE_: Tuple  		=       pad_along_height - pad_top

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(_UpperCAmelCase				,	_UpperCAmelCase				,	"constant"				,	0.0    )



class   __lowercase				( nn.Module	):




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[bool] = True   ,						lowerCAmelCase__  :     Optional[bool or str] = True   ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       config

									if in_channels % groups != 0:
															raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups.")
									if out_channels % groups != 0:
															raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups.")

									SCREAMING_SNAKE_CASE_: int  		=       0 if config.tf_padding else int((kernel_size - 1) / 2)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       nn.Convad(
									    in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=lowerCAmelCase__   ,						stride=lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						groups=lowerCAmelCase__   ,						bias=lowerCAmelCase__   ,						padding_mode="zeros"   ,						)

									if use_normalization:
															SCREAMING_SNAKE_CASE_: str  		=       nn.BatchNormad(
															    num_features=lowerCAmelCase__   ,						eps=config.layer_norm_eps   ,						momentum=0.9997   ,						affine=lowerCAmelCase__   ,						track_running_stats=lowerCAmelCase__   ,						)
									else:
															SCREAMING_SNAKE_CASE_: str  		=       None

									if use_activation:
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[use_activation]
															elif isinstance(config.hidden_act   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[config.hidden_act]
															else:
																					SCREAMING_SNAKE_CASE_: Any  		=       config.hidden_act
									else:
															SCREAMING_SNAKE_CASE_: int  		=       None




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     torch.Tensor):
									if self.config.tf_padding:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       apply_tf_padding(lowerCAmelCase__   ,						self.convolution)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.convolution(lowerCAmelCase__)
									if self.normalization is not None:
															SCREAMING_SNAKE_CASE_: int  		=       self.normalization(lowerCAmelCase__)
									if self.activation is not None:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       self.activation(lowerCAmelCase__)
									return features







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     MobileNetVaConfig
			_UpperCAmelCase   :							List[Any]										=     load_tf_weights_in_mobilenet_va
			_UpperCAmelCase   :							List[Any]										=     '''mobilenet_v1'''
			_UpperCAmelCase   :							int										=     '''pixel_values'''
			_UpperCAmelCase   :							List[Any]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Union[nn.Linear, nn.Convad]):
									if isinstance(lowerCAmelCase__   ,						(nn.Linear, nn.Convad)):
															module.weight.data.normal_(mean=0.0   ,						std=self.config.initializer_range)
															if module.bias is not None:
																					module.bias.data.zero_()
									elif isinstance(lowerCAmelCase__   ,						nn.BatchNormad):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0)







lowerCAmelCase							:  Any   	=						R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase							:  List[str]   	=						R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''		,       UpperCAmelCase_		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     bool = True):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       config

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       32
									SCREAMING_SNAKE_CASE_: Dict  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

									SCREAMING_SNAKE_CASE_: Tuple  		=       MobileNetVaConvLayer(
									    lowerCAmelCase__   ,						in_channels=config.num_channels   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=2   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

									SCREAMING_SNAKE_CASE_: str  		=       nn.ModuleList()
									for i in range(13):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       out_channels

															if strides[i] == 2 or i == 0:
																					depth *= 2
																					SCREAMING_SNAKE_CASE_: str  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

															self.layer.append(
															    MobileNetVaConvLayer(
															        lowerCAmelCase__   ,						in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=strides[i]   ,						groups=lowerCAmelCase__   ,						))

															self.layer.append(
															    MobileNetVaConvLayer(
															        lowerCAmelCase__   ,						in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=1   ,						))

									SCREAMING_SNAKE_CASE_: List[str]  		=       nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None

									# Initialize weights and apply final processing
									self.post_init()



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str):
									raise NotImplementedError




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						modality="vision"   ,						expected_output=_EXPECTED_OUTPUT_SHAPE   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_: Any  		=       return_dict if return_dict is not None else self.config.use_return_dict

									if pixel_values is None:
															raise ValueError("You have to specify pixel_values")

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.conv_stem(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       () if output_hidden_states else None

									for i, layer_module in enumerate(self.layer):
															SCREAMING_SNAKE_CASE_: Tuple  		=       layer_module(lowerCAmelCase__)

															if output_hidden_states:
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       all_hidden_states + (hidden_states,)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_states

									if self.pooler is not None:
															SCREAMING_SNAKE_CASE_: int  		=       torch.flatten(self.pooler(lowerCAmelCase__)   ,						start_dim=1)
									else:
															SCREAMING_SNAKE_CASE_: List[str]  		=       None

									if not return_dict:
															return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=lowerCAmelCase__   ,						pooler_output=lowerCAmelCase__   ,						hidden_states=lowerCAmelCase__   ,						)







@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,       UpperCAmelCase_		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Any   ,						lowerCAmelCase__  :     MobileNetVaConfig):
									super().__init__(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.num_labels
									SCREAMING_SNAKE_CASE_: Dict  		=       MobileNetVaModel(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.mobilenet_va.layer[-1].convolution.out_channels

									# Classifier head
									SCREAMING_SNAKE_CASE_: str  		=       nn.Dropout(config.classifier_dropout_prob   ,						inplace=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.Linear(lowerCAmelCase__   ,						config.num_labels) if config.num_labels > 0 else nn.Identity()

									# Initialize weights and apply final processing
									self.post_init()




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: List[str]  		=       return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.mobilenet_va(lowerCAmelCase__   ,						output_hidden_states=lowerCAmelCase__   ,						return_dict=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       outputs.pooler_output if return_dict else outputs[1]

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.classifier(self.dropout(lowerCAmelCase__))

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       None
									if labels is not None:
															if self.config.problem_type is None:
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       "regression"
																					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											SCREAMING_SNAKE_CASE_: int  		=       "single_label_classification"
																					else:
																											SCREAMING_SNAKE_CASE_: str  		=       "multi_label_classification"

															if self.config.problem_type == "regression":
																					SCREAMING_SNAKE_CASE_: Dict  		=       MSELoss()
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: Any  		=       loss_fct(logits.squeeze()   ,						labels.squeeze())
																					else:
																											SCREAMING_SNAKE_CASE_: int  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)
															elif self.config.problem_type == "single_label_classification":
																					SCREAMING_SNAKE_CASE_: Any  		=       CrossEntropyLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(logits.view(-1   ,						self.num_labels)   ,						labels.view(-1))
															elif self.config.problem_type == "multi_label_classification":
																					SCREAMING_SNAKE_CASE_: Dict  		=       BCEWithLogitsLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)

									if not return_dict:
															SCREAMING_SNAKE_CASE_: int  		=       (logits,) + outputs[2:]
															return ((loss,) + output) if loss is not None else output

									return ImageClassifierOutputWithNoAttention(
									    loss=lowerCAmelCase__   ,						logits=lowerCAmelCase__   ,						hidden_states=outputs.hidden_states   ,						)






