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
      UpperCamelCase  							=						'''platform'''

      import jax
      import jax.numpy as jnp

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

def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       str=None		,	_lowerCamelCase							:       int=None		,	_lowerCamelCase							:       str=None		,	_lowerCamelCase							:       Optional[Any]=None		,	_lowerCamelCase							:       str=None		,	_lowerCamelCase							:       str=None		,	):
    if attention_mask is None:
        lowercase__ :   Dict    					=				np.where(input_ids != config.pad_token_id		,	1		,	0)
    if decoder_attention_mask is None:
        lowercase__ :   Optional[Any]    					=				np.where(decoder_input_ids != config.pad_token_id		,	1		,	0)
    if head_mask is None:
        lowercase__ :   str    					=				np.ones((config.encoder_layers, config.encoder_attention_heads))
    if decoder_head_mask is None:
        lowercase__ :   Dict    					=				np.ones((config.decoder_layers, config.decoder_attention_heads))
    if cross_attn_head_mask is None:
        lowercase__ :   int    					=				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   snake_case_       :






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






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Tuple:
       lowercase__ :   Any    					=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,						self.vocab_size  )	,						3	,						self.vocab_size  )
       lowercase__ :   Tuple    					=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,						dtype=np.intaa  ))	,						-1  )

       lowercase__ :   Tuple    					=				shift_tokens_right(lowercase_	,						1	,						2  )

       lowercase__ :   Union[str, Any]    					=				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=lowercase_	,						)
       lowercase__ :   List[Any]    					=				prepare_blenderbot_inputs_dict(lowercase_	,						lowercase_	,						lowercase_  )
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     Tuple  )					->						int:
       lowercase__					,   lowercase__ :   List[Any]    					=				self.prepare_config_and_inputs()
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     List[Any]	,						lowercase_				:     Any	,						lowercase_				:     List[Any]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				20
       lowercase__ :   List[Any]    					=				model_class_name(lowercase_  )

       lowercase__ :   Optional[int]    					=				model.encode(inputs_dict["input_ids"]  )

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

       lowercase__ :   Optional[int]    					=				model.init_cache(decoder_input_ids.shape[0]	,						lowercase_	,						lowercase_  )
       lowercase__ :   Tuple    					=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,						dtype="i4"  )

       lowercase__ :   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)	,						)
       lowercase__ :   int    					=				model.decode(
           decoder_input_ids[:, :-1]	,						lowercase_	,						decoder_attention_mask=lowercase_	,						past_key_values=lowercase_	,						decoder_position_ids=lowercase_	,						)

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

       lowercase__ :   Union[str, Any]    					=				model.decode(lowercase_	,						lowercase_  )

       lowercase__ :   Tuple    					=				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				:     Any	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Dict	,						lowercase_				:     Any  )					->						Union[str, Any]:
       lowercase__ :   Optional[Any]    					=				20
       lowercase__ :   List[str]    					=				model_class_name(lowercase_  )

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

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

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

       lowercase__ :   List[str]    					=				model.init_cache(decoder_input_ids.shape[0]	,						lowercase_	,						lowercase_  )
       lowercase__ :   Tuple    					=				jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]	,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,						)

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

       lowercase__ :   int    					=				model.decode(lowercase_	,						lowercase_	,						decoder_attention_mask=lowercase_  )

       lowercase__ :   Dict    					=				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   snake_case_       (		unittest.TestCase       ):
   __A	:		Dict         =				99






   def       __UpperCamelCase     (      self				:     Any  )					->						Tuple:
       lowercase__ :   Tuple    					=				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	,						)

       lowercase__ :   Tuple    					=				input_ids.shape[0]
       lowercase__ :   Union[str, Any]    					=				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       __UpperCamelCase     (      self				:     str  )					->						Tuple:
       lowercase__					,   lowercase__					,   lowercase__ :   Optional[int]    					=				self._get_config_and_data()
       lowercase__ :   Tuple    					=				FlaxBlenderbotSmallForConditionalGeneration(lowercase_  )
       lowercase__ :   List[str]    					=				lm_model(input_ids=lowercase_  )
       lowercase__ :   int    					=				(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape	,						lowercase_  )






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







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



@require_flax
class   snake_case_       (		__A  ,unittest.TestCase  ,__A       ):
   __A	:		List[str]         =				True
   __A	:		List[str]         =				(
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   __A	:		str         =				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






   def       __UpperCamelCase     (      self				:     Any  )					->						List[str]:
       lowercase__ :   Dict    					=				FlaxBlenderbotSmallModelTester(self  )






   def       __UpperCamelCase     (      self				:     str  )					->						List[str]:
       lowercase__					,   lowercase__ :   Any    					=				self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(lowercase_	,						lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Any  )					->						List[str]:
       lowercase__					,   lowercase__ :   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(lowercase_	,						lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Tuple  )					->						List[str]:
       lowercase__					,   lowercase__ :   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__  ):
               lowercase__ :   Optional[int]    					=				self._prepare_for_class(lowercase_	,						lowercase_  )
               lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )

               @jax.jit
               def encode_jitted(lowercase_				:     Any	,						lowercase_				:     List[Any]=None	,						**lowercase_				:     Dict  ):
                   return model.encode(input_ids=lowercase_	,						attention_mask=lowercase_  )

               with self.subTest("JIT Enabled"  ):
                   lowercase__ :   List[str]    					=				encode_jitted(**lowercase_  ).to_tuple()

               with self.subTest("JIT Disabled"  ):
                   with jax.disable_jit():
                       lowercase__ :   Any    					=				encode_jitted(**lowercase_  ).to_tuple()

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Optional[int]:
       lowercase__					,   lowercase__ :   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__  ):
               lowercase__ :   int    					=				model_class(lowercase_  )
               lowercase__ :   List[str]    					=				model.encode(inputs_dict["input_ids"]	,						inputs_dict["attention_mask"]  )

               lowercase__ :   int    					=				{
                   "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(lowercase_				:     Any	,						lowercase_				:     int	,						lowercase_				:     Optional[int]  ):
                   return model.decode(
                       decoder_input_ids=lowercase_	,						decoder_attention_mask=lowercase_	,						encoder_outputs=lowercase_	,						)

               with self.subTest("JIT Enabled"  ):
                   lowercase__ :   Any    					=				decode_jitted(**lowercase_  ).to_tuple()

               with self.subTest("JIT Disabled"  ):
                   with jax.disable_jit():
                       lowercase__ :   Tuple    					=				decode_jitted(**lowercase_  ).to_tuple()

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







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







"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




