



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
      from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Tuple  		=					self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""hidden_sizes"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_attention_heads"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_encoder_blocks"""							)							)







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     int  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]=13  ,lowerCamelCase_:     Dict=64  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     List[str]=4  ,lowerCamelCase_:     Optional[int]=[2, 2, 2, 2]  ,lowerCamelCase_:     Optional[int]=[8, 4, 2, 1]  ,lowerCamelCase_:     Any=[16, 32, 64, 128]  ,lowerCamelCase_:     int=[1, 4, 8, 16]  ,lowerCamelCase_:     Union[str, Any]=[1, 2, 4, 8]  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Any=True  ,lowerCamelCase_:     Optional[Any]="gelu"  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     List[str]=0.0_2  ,lowerCamelCase_:     Optional[Any]=16  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     int=None  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					parent
       UpperCAmelCase_     :       Tuple  		=					batch_size
       UpperCAmelCase_     :       Optional[int]  		=					image_size
       UpperCAmelCase_     :       Dict  		=					num_channels
       UpperCAmelCase_     :       Union[str, Any]  		=					num_encoder_blocks
       UpperCAmelCase_     :       int  		=					sr_ratios
       UpperCAmelCase_     :       int  		=					depths
       UpperCAmelCase_     :       str  		=					hidden_sizes
       UpperCAmelCase_     :       Union[str, Any]  		=					downsampling_rates
       UpperCAmelCase_     :       List[str]  		=					num_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					is_training
       UpperCAmelCase_     :       int  		=					use_labels
       UpperCAmelCase_     :       Optional[Any]  		=					hidden_act
       UpperCAmelCase_     :       Optional[int]  		=					hidden_dropout_prob
       UpperCAmelCase_     :       List[str]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Any  		=					initializer_range
       UpperCAmelCase_     :       List[str]  		=					decoder_hidden_size
       UpperCAmelCase_     :       Any  		=					num_labels
       UpperCAmelCase_     :       Optional[int]  		=					scope


   def       A__    (    self:     List[str]							)	->      int:
       UpperCAmelCase_     :       Dict  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCAmelCase_     :       Optional[Any]  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       Any  		=					ids_tensor([self.batch_size, self.image_size, self.image_size]  ,self.num_labels							)

       UpperCAmelCase_     :       str  		=					self.get_config()
       return config, pixel_values, labels


   def       A__    (    self:     Union[str, Any]							)	->      int:
       return GLPNConfig(
           image_size=self.image_size  ,num_channels=self.num_channels  ,num_encoder_blocks=self.num_encoder_blocks  ,depths=self.depths  ,hidden_sizes=self.hidden_sizes  ,num_attention_heads=self.num_attention_heads  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,initializer_range=self.initializer_range  ,decoder_hidden_size=self.decoder_hidden_size  ,)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      Any:
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNModel(config=lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Any  		=					model(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					self.image_size // (self.downsampling_rates[-1] * 2)
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str							)	->      List[str]:
       UpperCAmelCase_     :       Optional[Any]  		=					self.num_labels
       UpperCAmelCase_     :       Any  		=					GLPNForDepthEstimation(lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_  ,labels=lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)






   def       A__    (    self:     Tuple							)	->      Tuple:
       UpperCAmelCase_     :       Optional[int]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					config_and_inputs
       UpperCAmelCase_     :       Optional[Any]  		=					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : int       =						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   A__      : Any       =						(
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   A__      : List[str]       =						False
   A__      : Optional[int]       =						False
   A__      : str       =						False


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					GLPNModelTester(self							)
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNConfigTester(self  ,config_class=lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      Optional[Any]:
       self.config_tester.run_common_tests()


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_							)


   @unittest.skip("""GLPN does not use inputs_embeds"""							)
   def       A__    (    self:     List[str]							)	->      List[Any]:
       pass


   @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""							)
   def       A__    (    self:     Optional[Any]							)	->      Optional[Any]:
       pass


   def       A__    (    self:     Union[str, Any]							)	->      List[str]:
       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Tuple  		=					model_class(lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCAmelCase_     :       Dict  		=					[*signature.parameters.keys()]

           UpperCAmelCase_     :       str  		=					["""pixel_values"""]
           self.assertListEqual(arg_names[:1]  ,lowerCamelCase_							)


   def       A__    (    self:     str							)	->      List[Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       str  		=					True

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       int  		=					True
           UpperCAmelCase_     :       Dict  		=					False
           UpperCAmelCase_     :       Optional[int]  		=					True
           UpperCAmelCase_     :       List[str]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       Tuple  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)
           UpperCAmelCase_     :       List[Any]  		=					outputs.attentions

           UpperCAmelCase_     :       str  		=					sum(self.model_tester.depths							)
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCAmelCase_     :       int  		=					True
           UpperCAmelCase_     :       Optional[Any]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       List[str]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)
           UpperCAmelCase_     :       Optional[Any]  		=					outputs.attentions

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
           self.assertListEqual(
               list(attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)

           # verify the last attentions (last block, last layer)
           UpperCAmelCase_     :       Tuple  		=					(self.model_tester.image_size // 32) ** 2
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
           self.assertListEqual(
               list(attentions[-1].shape[-3:]							)  ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]  ,)
           UpperCAmelCase_     :       Tuple  		=					len(lowerCamelCase_							)

           # Check attention is always last and order is fine
           UpperCAmelCase_     :       Optional[Any]  		=					True
           UpperCAmelCase_     :       List[str]  		=					True
           UpperCAmelCase_     :       str  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()
           with torch.no_grad():
               UpperCAmelCase_     :       str  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)

           self.assertEqual(out_len + 1  ,len(lowerCamelCase_							)							)

           UpperCAmelCase_     :       List[str]  		=					outputs.attentions

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)
           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
           self.assertListEqual(
               list(self_attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)


   def       A__    (    self:     str							)	->      List[Any]:
       def check_hidden_states_output(lowerCamelCase_:     Any  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]							):
           UpperCAmelCase_     :       Optional[int]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()

           with torch.no_grad():
               UpperCAmelCase_     :       Optional[int]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)

           UpperCAmelCase_     :       List[str]  		=					outputs.hidden_states

           UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.num_encoder_blocks
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]							)  ,[
                   self.model_tester.hidden_sizes[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]  ,)

       UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Optional[Any]  		=					True
           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCAmelCase_     :       Tuple  		=					True

           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      List[str]:
       if not self.model_tester.is_training:
           return

       UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       Dict  		=					True

       for model_class in self.all_model_classes:
           if model_class in get_values(lowerCamelCase_							):
               continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           if model_class.__name__ == "GLPNForDepthEstimation":
               UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					inputs_dict["""pixel_values"""].shape
               UpperCAmelCase_     :       Optional[int]  		=					torch.zeros(
                   [self.model_tester.batch_size, height, width]  ,device=lowerCamelCase_							).long()
           UpperCAmelCase_     :       int  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.train()
           UpperCAmelCase_     :       Union[str, Any]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           UpperCAmelCase_     :       Optional[Any]  		=					model(**lowerCamelCase_							).loss
           loss.backward()






   @slow
   def       A__    (    self:     List[str]							)	->      Optional[int]:
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       List[Any]  		=					GLPNModel.from_pretrained(lowerCamelCase_							)
           self.assertIsNotNone(lowerCamelCase_							)







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image







@require_torch
@require_vision
@slow
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   @slow
   def       A__    (    self:     Any							)	->      List[str]:
       UpperCAmelCase_     :       str  		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
       UpperCAmelCase_     :       Tuple  		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					prepare_img()
       UpperCAmelCase_     :       List[Any]  		=					image_processor(images=lowerCamelCase_  ,return_tensors="""pt"""							).to(lowerCamelCase_							)

       # forward pass
       with torch.no_grad():
           UpperCAmelCase_     :       Union[str, Any]  		=					model(**lowerCamelCase_							)

       # verify the predicted depth
       UpperCAmelCase_     :       Tuple  		=					torch.Size([1, 480, 640]							)
       self.assertEqual(outputs.predicted_depth.shape  ,lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					torch.tensor(
           [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(lowerCamelCase_							)

       self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]  ,lowerCamelCase_  ,atol=1e-4							)							)




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch




@require_flax
class  A_ :







 """simple docstring"""





 def __init__(      self       :			Tuple							,     _a       :			Optional[int]							,     _a       :			Tuple=1_4							,     _a       :			List[Any]=7							,     _a       :			List[str]=True							,     _a       :			Tuple=True							,     _a       :			Any=True							,     _a       :			str=9_9							,     _a       :			str=3_2							,     _a       :			Optional[Any]=5							,     _a       :			int=4							,     _a       :			Union[str, Any]=3_7							,     _a       :			List[Any]="gelu"							,     _a       :			List[Any]=0.1							,     _a       :			Tuple=0.1							,     _a       :			int=5_1_2							,     _a       :			Tuple=0.0_2							,     _a       :			Dict=None							,     )     ->      Tuple:
      UpperCAmelCase    :					List[Any]			=      parent
      UpperCAmelCase    :					str			=      batch_size
      UpperCAmelCase    :					Dict			=      seq_length
      UpperCAmelCase    :					Any			=      is_training
      UpperCAmelCase    :					Union[str, Any]			=      use_input_mask
      UpperCAmelCase    :					int			=      use_labels
      UpperCAmelCase    :					Optional[Any]			=      vocab_size
      UpperCAmelCase    :					Union[str, Any]			=      d_model
      UpperCAmelCase    :					Optional[Any]			=      num_hidden_layers
      UpperCAmelCase    :					Tuple			=      num_attention_heads
      UpperCAmelCase    :					str			=      ffn_dim
      UpperCAmelCase    :					Dict			=      activation_function
      UpperCAmelCase    :					int			=      activation_dropout
      UpperCAmelCase    :					Union[str, Any]			=      attention_dropout
      UpperCAmelCase    :					Optional[int]			=      max_position_embeddings
      UpperCAmelCase    :					str			=      initializer_range
      UpperCAmelCase    :					Tuple			=      None
      UpperCAmelCase    :					List[Any]			=      0
      UpperCAmelCase    :					List[str]			=      2
      UpperCAmelCase    :					Dict			=      1
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      List[str]:
      UpperCAmelCase    :					str			=      np.clip(ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size					)							,     3							,     self.vocab_size					)

      UpperCAmelCase    :					Union[str, Any]			=      None
      if self.use_input_mask:
           UpperCAmelCase    :					int			=      random_attention_mask([self.batch_size, self.seq_length]					)

      UpperCAmelCase    :					Union[str, Any]			=      XGLMConfig(
          vocab_size=self.vocab_size							,     d_model=self.hidden_size							,     num_layers=self.num_hidden_layers							,     attention_heads=self.num_attention_heads							,     ffn_dim=self.ffn_dim							,     activation_function=self.activation_function							,     activation_dropout=self.activation_dropout							,     attention_dropout=self.attention_dropout							,     max_position_embeddings=self.max_position_embeddings							,     initializer_range=self.initializer_range							,     use_cache=_a							,     bos_token_id=self.bos_token_id							,     eos_token_id=self.eos_token_id							,     pad_token_id=self.pad_token_id							,     )

      return (config, input_ids, input_mask)
 def 	_UpperCamelCase     (      self       :			str					)     ->      List[str]:
      UpperCAmelCase    :					Tuple			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      config_and_inputs
      UpperCAmelCase    :					str			=      {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      List[str]:
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      self.prepare_config_and_inputs()

      UpperCAmelCase    :					List[Any]			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      UpperCAmelCase    :					Any			=      ids_tensor([self.batch_size, self.seq_length]							,     vocab_size=2					)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Optional[int]							,     _a       :			Dict							,     _a       :			Tuple							,     _a       :			Dict					)     ->      str:
      UpperCAmelCase    :					List[str]			=      2_0
      UpperCAmelCase    :					Optional[Any]			=      model_class_name(_a					)

      UpperCAmelCase    :					Dict			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					List[Any]			=      jnp.ones((input_ids.shape[0], max_decoder_length)							,     dtype="i4"					)

      UpperCAmelCase    :					str			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)
      UpperCAmelCase    :					List[str]			=      model(
          input_ids[:, :-1]							,     attention_mask=_a							,     past_key_values=_a							,     position_ids=_a							,     )

      UpperCAmelCase    :					Any			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="i4"					)
      UpperCAmelCase    :					Dict			=      model(
          input_ids[:, -1:]							,     attention_mask=_a							,     past_key_values=outputs_cache.past_key_values							,     position_ids=_a							,     )

      UpperCAmelCase    :					int			=      model(_a					)

      UpperCAmelCase    :					Optional[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 	_UpperCamelCase     (      self       :			str							,     _a       :			List[Any]							,     _a       :			Any							,     _a       :			Optional[Any]							,     _a       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					List[Any]			=      2_0
      UpperCAmelCase    :					Any			=      model_class_name(_a					)

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

      UpperCAmelCase    :					Optional[int]			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					int			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)

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

      UpperCAmelCase    :					Union[str, Any]			=      model(_a							,     attention_mask=_a					)
      UpperCAmelCase    :					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_sentencepiece
@require_flax
class  A_ (			a_   ,    a_   ,    unittest.TestCase  ):







 """simple docstring"""





 _UpperCamelCase					:			Tuple     	=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 _UpperCamelCase					:			int     	=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					Any			=      FlaxXGLMModelTester(self					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_a							,     _a							,     _a							,     _a					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Tuple			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _a							,     _a							,     _a							,     _a					)
 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      UpperCAmelCase    :					int			=      XGLMTokenizer.from_pretrained("XGLM"							,     padding_side="left"					)
      UpperCAmelCase    :					Optional[int]			=      tokenizer(["Hello this is a long string", "Hey"]							,     return_tensors="np"							,     padding=_a							,     truncation=_a					)

      UpperCAmelCase    :					int			=      FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
      UpperCAmelCase    :					Optional[Any]			=      1
      UpperCAmelCase    :					Tuple			=      False

      UpperCAmelCase    :					Any			=      jax.jit(model.generate					)

      UpperCAmelCase    :					Any			=      jit_generate(inputs["input_ids"]							,     attention_mask=inputs["attention_mask"]					).sequences

      UpperCAmelCase    :					Any			=      tokenizer.batch_decode(_a							,     skip_special_tokens=_a					)

      UpperCAmelCase    :					Union[str, Any]			=      [
          "Hello this is a long string of questions, but I'm not sure if I'm",
          "Hey, I'm a newbie to the forum and I'",
      ]

      self.assertListEqual(_a							,     _a					)
 @is_pt_flax_cross_test
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[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
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					int			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					str			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					str			=      0
                     UpperCAmelCase    :					Optional[Any]			=      1
                     UpperCAmelCase    :					int			=      0
                     UpperCAmelCase    :					Union[str, Any]			=      1
                UpperCAmelCase    :					Dict			=      pt_model_class(_a					).eval()
                # Flax models don't use the `use_cache` option and cache is not returned as a default.
                # So we disable `use_cache` here for PyTorch model.
                UpperCAmelCase    :					List[Any]			=      False
                UpperCAmelCase    :					Union[str, Any]			=      model_class(_a							,     dtype=jnp.floataa					)
                UpperCAmelCase    :					Union[str, Any]			=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,     _a					)
                UpperCAmelCase    :					List[Any]			=      fx_state

                with torch.no_grad():
                     UpperCAmelCase    :					str			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					int			=      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					)
                     UpperCAmelCase    :					Optional[Any]			=      model_class.from_pretrained(_a							,     from_pt=_a					)

                UpperCAmelCase    :					Optional[Any]			=      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       :			int					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[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
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					Optional[Any]			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                UpperCAmelCase    :					Optional[Any]			=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCAmelCase    :					Dict			=      getattr(_a							,     _a					)

                UpperCAmelCase    :					Optional[int]			=      pt_model_class(_a					).eval()
                UpperCAmelCase    :					int			=      False
                UpperCAmelCase    :					int			=      model_class(_a							,     dtype=jnp.floataa					)

                UpperCAmelCase    :					List[Any]			=      load_flax_weights_in_pytorch_model(_a							,     fx_model.params					)
                UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					int			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					Dict			=      0
                     UpperCAmelCase    :					Any			=      1
                     UpperCAmelCase    :					Optional[int]			=      0
                     UpperCAmelCase    :					Optional[int]			=      1

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

                with torch.no_grad():
                     UpperCAmelCase    :					int			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					Optional[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:
                     fx_model.save_pretrained(_a					)
                     UpperCAmelCase    :					List[Any]			=      pt_model_class.from_pretrained(_a							,     from_flax=_a					)

                with torch.no_grad():
                     UpperCAmelCase    :					List[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					)

 @slow
 def 	_UpperCamelCase     (      self       :			Any					)     ->      str:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase    :					Dict			=      model_class_name.from_pretrained("facebook/xglm-564M"					)
           UpperCAmelCase    :					List[Any]			=      model(np.ones((1, 1)					)					)
           self.assertIsNotNone(_a					)



