import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
		import torch






class 		a__						:

					def __init__(    self      :       int,_A      :       Tuple,_A      :       Union[str, Any]=14,_A      :       int=7,_A      :       Tuple=True,_A      :       Tuple=True,_A      :       int=False,_A      :       Dict=True,_A      :       List[Any]=99,_A      :       List[Any]=32,_A      :       Union[str, Any]=4,_A      :       List[str]=4,_A      :       str=4,_A      :       List[Any]=37,_A      :       int="gelu",_A      :       str=0.1,_A      :       Tuple=0.1,_A      :       Tuple=512,_A      :       int=0.02,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Any				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Dict				      =    is_training
							SCREAMING_SNAKE_CASE_  :		str				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		Any				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		int				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    rotary_dim
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		Any				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    None
							SCREAMING_SNAKE_CASE_  :		str				      =    vocab_size - 1
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    vocab_size - 1
							SCREAMING_SNAKE_CASE_  :		int				      =    vocab_size - 1

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    random_attention_mask([self.batch_size, self.seq_length]    )

							SCREAMING_SNAKE_CASE_  :		int				      =    GPTJConfig(
							    vocab_size=self.vocab_size,n_embd=self.hidden_size,n_layer=self.num_hidden_layers,n_head=self.num_attention_heads,n_positions=self.max_position_embeddings,use_cache=_A,bos_token_id=self.bos_token_id,eos_token_id=self.eos_token_id,pad_token_id=self.pad_token_id,rotary_dim=self.rotary_dim,)

							return (config, input_ids, input_mask)

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict

					def 				__UpperCamelCase   (    self      :       int,_A      :       Tuple,_A      :       str,_A      :       Union[str, Any],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    20
							SCREAMING_SNAKE_CASE_  :		Any				      =    model_class_name(_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model.init_cache(input_ids.shape[0],_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    jnp.ones((input_ids.shape[0], max_decoder_length),dtype="i4"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1    )[None, :],(input_ids.shape[0], input_ids.shape[-1] - 1)    )
							SCREAMING_SNAKE_CASE_  :		str				      =    model(
							    input_ids[:, :-1],attention_mask=_A,past_key_values=_A,position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype="i4"    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(
							    input_ids[:, -1:],attention_mask=_A,past_key_values=outputs_cache.past_key_values,position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       int,_A      :       Union[str, Any],_A      :       int,_A      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    20
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_class_name(_A    )

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

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model.init_cache(input_ids.shape[0],_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1    )[None, :],(input_ids.shape[0], input_ids.shape[-1] - 1)    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(
							    input_ids[:, :-1],attention_mask=_A,past_key_values=_A,position_ids=_A,)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype="i4"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    model(
							    input_ids[:, -1:],past_key_values=outputs_cache.past_key_values,attention_mask=_A,position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A,attention_mask=_A    )

							SCREAMING_SNAKE_CASE_  :		int				      =    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 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					A	       =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    FlaxGPTJModelTester(self    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(_A,_A,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    _A,_A,_A,_A    )

					@tooslow
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    GPTaTokenizer.from_pretrained("gpt2",pad_token="<|endoftext|>",padding_side="left"    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tokenizer(["Hello this is a long string", "Hey"],return_tensors="np",padding=_A,truncation=_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"    )
							SCREAMING_SNAKE_CASE_  :		str				      =    False
							SCREAMING_SNAKE_CASE_  :		Any				      =    model.config.eos_token_id

							SCREAMING_SNAKE_CASE_  :		Dict				      =    jax.jit(model.generate    )

							SCREAMING_SNAKE_CASE_  :		str				      =    jit_generate(
							    inputs["input_ids"],attention_mask=inputs["attention_mask"],pad_token_id=tokenizer.pad_token_id    ).sequences

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer.batch_decode(_A,skip_special_tokens=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [
							    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
							    "Hey, I'm a little late to the party. I'm going to",
							]

							self.assertListEqual(_A,_A    )

					@is_pt_flax_cross_test
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__    ):
											# prepare inputs
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    self._prepare_for_class(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											SCREAMING_SNAKE_CASE_  :		str				      =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
											SCREAMING_SNAKE_CASE_  :		str				      =    getattr(_A,_A    )

											SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    pt_inputs["input_ids"].shape
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.randint(0,seq_length - 1,size=(batch_size,)    )
											for batch_idx, start_index in enumerate(_A    ):
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
													SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    1
													SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    0
													SCREAMING_SNAKE_CASE_  :		Any				      =    1
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    pt_model_class(_A    ).eval()
											SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class(_A,dtype=jnp.floataa    )

											SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    convert_pytorch_state_dict_to_flax(pt_model.state_dict(),_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    fx_state

											with torch.no_grad():
													SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    pt_model(**_A    ).to_tuple()

											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    fx_model(**_A    ).to_tuple()
											self.assertEqual(len(_A    ),len(_A    ),"Output lengths differ between Flax and PyTorch"    )
											for fx_output, pt_output in zip(_A,_A    ):
													self.assert_almost_equals(fx_output[:, -1],pt_output[:, -1].numpy(),4E-2    )

											with tempfile.TemporaryDirectory() as tmpdirname:
													pt_model.save_pretrained(_A    )
													SCREAMING_SNAKE_CASE_  :		int				      =    model_class.from_pretrained(_A,from_pt=_A    )

											SCREAMING_SNAKE_CASE_  :		str				      =    fx_model_loaded(**_A    ).to_tuple()
											self.assertEqual(
											    len(_A    ),len(_A    ),"Output lengths differ between Flax and PyTorch"    )
											for fx_output_loaded, pt_output in zip(_A,_A    ):
													self.assert_almost_equals(fx_output_loaded[:, -1],pt_output[:, -1].numpy(),4E-2    )

					@is_pt_flax_cross_test
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__    ):
											# prepare inputs
											SCREAMING_SNAKE_CASE_  :		str				      =    self._prepare_for_class(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    getattr(_A,_A    )

											SCREAMING_SNAKE_CASE_  :		List[str]				      =    pt_model_class(_A    ).eval()
											SCREAMING_SNAKE_CASE_  :		Any				      =    model_class(_A,dtype=jnp.floataa    )

											SCREAMING_SNAKE_CASE_  :		Tuple				      =    load_flax_weights_in_pytorch_model(_A,fx_model.params    )
											SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    pt_inputs["input_ids"].shape
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.random.randint(0,seq_length - 1,size=(batch_size,)    )
											for batch_idx, start_index in enumerate(_A    ):
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    1
													SCREAMING_SNAKE_CASE_  :		int				      =    0
													SCREAMING_SNAKE_CASE_  :		int				      =    1

											# make sure weights are tied in PyTorch
											pt_model.tie_weights()

											with torch.no_grad():
													SCREAMING_SNAKE_CASE_  :		Any				      =    pt_model(**_A    ).to_tuple()

											SCREAMING_SNAKE_CASE_  :		List[str]				      =    fx_model(**_A    ).to_tuple()
											self.assertEqual(len(_A    ),len(_A    ),"Output lengths differ between Flax and PyTorch"    )
											for fx_output, pt_output in zip(_A,_A    ):
													self.assert_almost_equals(fx_output[:, -1],pt_output[:, -1].numpy(),4E-2    )

											with tempfile.TemporaryDirectory() as tmpdirname:
													fx_model.save_pretrained(_A    )
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    pt_model_class.from_pretrained(_A,from_flax=_A    )

											with torch.no_grad():
													SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    pt_model_loaded(**_A    ).to_tuple()

											self.assertEqual(
											    len(_A    ),len(_A    ),"Output lengths differ between Flax and PyTorch"    )
											for fx_output, pt_output in zip(_A,_A    ):
													self.assert_almost_equals(fx_output[:, -1],pt_output[:, -1].numpy(),4E-2    )






					@tooslow
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		str				      =    model_class_name.from_pretrained("EleutherAI/gpt-j-6B"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(np.ones((1, 1)    )    )
									self.assertIsNotNone(_A    )





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a          =		{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TimesformerModel",
           "TimesformerForVideoClassification",
           "TimesformerPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timesformer import (
                  TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TimesformerForVideoClassification,
                  TimesformerModel,
                  TimesformerPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
