
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				=			"""platform"""

   import jax
   import jax.numpy as jnp

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


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[int]=None							,   __lowerCamelCase	:					Optional[int]=None							,   __lowerCamelCase	:					List[Any]=None							,   __lowerCamelCase	:					str=None							,   __lowerCamelCase	:					str=None							,   __lowerCamelCase	:					int=None							,   ):
       if attention_mask is None:
              snake_case			:  Union[str, Any]     							= np.where(input_ids != config.pad_token_id							,   1							,   0       )
       if decoder_attention_mask is None:
              snake_case			:  Dict     							= np.where(decoder_input_ids != config.pad_token_id							,   1							,   0       )
       if head_mask is None:
              snake_case			:  Optional[int]     							= np.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              snake_case			:  Tuple     							= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              snake_case			:  List[str]     							= 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     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Optional[Any]=13       ,		snake_case__						:   Union[str, Any]=7       ,		snake_case__						:   Optional[int]=True       ,		snake_case__						:   int=False       ,		snake_case__						:   Optional[Any]=99       ,		snake_case__						:   Any=16       ,		snake_case__						:   Union[str, Any]=2       ,		snake_case__						:   Tuple=4       ,		snake_case__						:   Any=4       ,		snake_case__						:   List[str]="gelu"       ,		snake_case__						:   Union[str, Any]=0.1       ,		snake_case__						:   List[str]=0.1       ,		snake_case__						:   List[str]=32       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[str]=0       ,		snake_case__						:   Any=0.02       ,		)						->      str:


          '''simple docstring'''


          snake_case			:  int     							= parent
          snake_case			:  Tuple     							= batch_size
          snake_case			:  Optional[int]     							= seq_length
          snake_case			:  Any     							= is_training
          snake_case			:  Any     							= use_labels
          snake_case			:  List[str]     							= vocab_size
          snake_case			:  Optional[Any]     							= hidden_size
          snake_case			:  Union[str, Any]     							= num_hidden_layers
          snake_case			:  Optional[int]     							= num_attention_heads
          snake_case			:  List[str]     							= intermediate_size
          snake_case			:  Any     							= hidden_act
          snake_case			:  Dict     							= hidden_dropout_prob
          snake_case			:  Union[str, Any]     							= attention_probs_dropout_prob
          snake_case			:  Tuple     							= max_position_embeddings
          snake_case			:  int     							= eos_token_id
          snake_case			:  Tuple     							= pad_token_id
          snake_case			:  str     							= bos_token_id
          snake_case			:  Optional[int]     							= initializer_range



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Union[str, Any]:


          '''simple docstring'''


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

          snake_case			:  str     							= shift_tokens_right(snake_case__       ,		1       ,		2					)

          snake_case			:  Tuple     							= 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=snake_case__       ,		)
          snake_case			:  List[str]     							= prepare_blenderbot_inputs_dict(snake_case__       ,		snake_case__       ,		snake_case__					)
          return config, inputs_dict



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          snake_case							,   snake_case			:  str     							= self.prepare_config_and_inputs()
          return config, inputs_dict



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Any       ,		snake_case__						:   Any					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  List[str]     							= 20
          snake_case			:  Any     							= model_class_name(snake_case__					)

          snake_case			:  Union[str, Any]     							= model.encode(inputs_dict["input_ids"]					)

          snake_case							,   snake_case			:  int     							= (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          snake_case			:  Optional[Any]     							= model.init_cache(decoder_input_ids.shape[0]       ,		snake_case__       ,		snake_case__					)
          snake_case			:  int     							= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,		dtype="i4"					)

          snake_case			:  List[str]     							= jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,		)
          snake_case			:  int     							= model.decode(
              decoder_input_ids[:, :-1]       ,		snake_case__       ,		decoder_attention_mask=snake_case__       ,		past_key_values=snake_case__       ,		decoder_position_ids=snake_case__       ,		)

          snake_case			:  int     							= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,		dtype="i4"					)
          snake_case			:  Dict     							= model.decode(
              decoder_input_ids[:, -1:]       ,		snake_case__       ,		decoder_attention_mask=snake_case__       ,		past_key_values=outputs_cache.past_key_values       ,		decoder_position_ids=snake_case__       ,		)

          snake_case			:  Union[str, Any]     							= model.decode(snake_case__       ,		snake_case__					)

          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						:   int       ,		snake_case__						:   str       ,		snake_case__						:   Dict       ,		snake_case__						:   str					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= 20
          snake_case			:  int     							= model_class_name(snake_case__					)

          snake_case			:  List[str]     							= model.encode(inputs_dict["input_ids"]					)

          snake_case							,   snake_case			:  Any     							= (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          snake_case			:  Tuple     							= jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
              ]       ,		axis=-1       ,		)

          snake_case			:  Optional[Any]     							= model.init_cache(decoder_input_ids.shape[0]       ,		snake_case__       ,		snake_case__					)
          snake_case			:  Optional[int]     							= jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,		)

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

          snake_case			:  Union[str, Any]     							= model.decode(snake_case__       ,		snake_case__       ,		decoder_attention_mask=snake_case__					)

          snake_case			:  str     							= 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     UpperCAmelCase						(		unittest.TestCase					):
   A__   :						Tuple			=	99



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          snake_case			:  List[Any]     							= 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       ,		)

          snake_case			:  Tuple     							= input_ids.shape[0]
          snake_case			:  Tuple     							= 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[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case							,   snake_case							,   snake_case			:  List[Any]     							= self._get_config_and_data()
          snake_case			:  int     							= FlaxBlenderbotSmallForConditionalGeneration(snake_case__					)
          snake_case			:  Tuple     							= lm_model(input_ids=snake_case__					)
          snake_case			:  str     							= (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["logits"].shape       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  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       ,		)
          snake_case			:  Optional[int]     							= FlaxBlenderbotSmallForConditionalGeneration(snake_case__					)
          snake_case			:  List[Any]     							= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,		dtype=np.intaa					)
          snake_case			:  str     							= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,		dtype=np.intaa					)
          snake_case			:  Optional[int]     							= lm_model(input_ids=snake_case__       ,		decoder_input_ids=snake_case__					)
          snake_case			:  Optional[Any]     							= (*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


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






@require_flax
class     UpperCAmelCase						(		A_			,unittest.TestCase			,A_					):
   A__   :						str			=	True
   A__   :						int			=	(
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__   :						int			=	(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Any     							= FlaxBlenderbotSmallModelTester(self					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      List[str]:


          '''simple docstring'''


          snake_case							,   snake_case			:  Union[str, Any]     							= self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      List[Any]:


          '''simple docstring'''


          snake_case							,   snake_case			:  Any     							= 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(snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case							,   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__					):
                        snake_case			:  Union[str, Any]     							= self._prepare_for_class(snake_case__       ,		snake_case__					)
                        snake_case			:  str     							= model_class(snake_case__					)

                        @jax.jit
                        def encode_jitted(snake_case__						:   Optional[int]       ,		snake_case__						:   Tuple=None       ,		**snake_case__						:   Optional[Any]					):
                               return model.encode(input_ids=snake_case__       ,		attention_mask=snake_case__					)

                        with self.subTest("JIT Enabled"					):
                               snake_case			:  Tuple     							= encode_jitted(**snake_case__					).to_tuple()

                        with self.subTest("JIT Disabled"					):
                               with jax.disable_jit():
                                      snake_case			:  List[str]     							= encode_jitted(**snake_case__					).to_tuple()

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          snake_case							,   snake_case			:  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__					):
                        snake_case			:  List[Any]     							= model_class(snake_case__					)
                        snake_case			:  Tuple     							= model.encode(inputs_dict["input_ids"]       ,		inputs_dict["attention_mask"]					)

                        snake_case			:  Optional[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(snake_case__						:   str       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Any					):
                               return model.decode(
                                   decoder_input_ids=snake_case__       ,		decoder_attention_mask=snake_case__       ,		encoder_outputs=snake_case__       ,		)

                        with self.subTest("JIT Enabled"					):
                               snake_case			:  Any     							= decode_jitted(**snake_case__					).to_tuple()

                        with self.subTest("JIT Disabled"					):
                               with jax.disable_jit():
                                      snake_case			:  int     							= decode_jitted(**snake_case__					).to_tuple()

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



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Tuple:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case			:  str     							= model_class_name.from_pretrained("facebook/blenderbot_small-90M"					)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 snake_case			:  Any     							= np.ones((1, 1)					) * model.config.eos_token_id
                 snake_case			:  Union[str, Any]     							= model(snake_case__					)
                 self.assertIsNotNone(snake_case__					)







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





