

"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__A  :					Optional[Any]												=	'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class     __UpperCamelCase				(     unittest.TestCase  ,	_A		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							A    =      load_tool("summarization")
							self.tool.setup()
							A    =      load_tool("summarization"  ,  remote=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      self.tool(__SCREAMING_SNAKE_CASE)
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      self.remote_tool(__SCREAMING_SNAKE_CASE)
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      self.tool(text=__SCREAMING_SNAKE_CASE)
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      self.remote_tool(text=__SCREAMING_SNAKE_CASE)
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."  ,  )








"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
	__A  :					Optional[int]												=	'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None   ):
					"""simple docstring"""
					if attention_mask is None:
										A    =      np.where(input_ids != config.pad_token_id    ,							1    ,							0   )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class     __UpperCamelCase				:


		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int=1_3  ,  __SCREAMING_SNAKE_CASE				:	List[str]=7  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	Dict=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=9_9  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=4  ,  __SCREAMING_SNAKE_CASE				:	Any=4  ,  __SCREAMING_SNAKE_CASE				:	str="gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Any=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=2_0  ,  __SCREAMING_SNAKE_CASE				:	Any=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1  ,  __SCREAMING_SNAKE_CASE				:	Any=0  ,  __SCREAMING_SNAKE_CASE				:	str=1_6  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  ):
							A    =      parent
							A    =      batch_size
							A    =      seq_length
							A    =      is_training
							A    =      use_labels
							A    =      vocab_size
							A    =      hidden_size
							A    =      num_hidden_layers
							A    =      num_attention_heads
							A    =      intermediate_size
							A    =      hidden_act
							A    =      hidden_dropout_prob
							A    =      attention_probs_dropout_prob
							A    =      max_position_embeddings
							A    =      eos_token_id
							A    =      pad_token_id
							A    =      bos_token_id
							A    =      embed_dim
							A    =      word_embed_proj_dim
							A    =      initializer_range
							A    =      False


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

							A    =      OPTConfig(
							    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  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  ,  embed_dim=self.embed_dim  ,  is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  word_embed_proj_dim=self.word_embed_proj_dim  ,  initializer_range=self.initializer_range  ,  use_cache=__SCREAMING_SNAKE_CASE  ,  )
							A    =      prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    ,					A    =      self.prepare_config_and_inputs()
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    =      inputs_dict["input_ids"]
							A    =      inputs_dict["attention_mask"]

							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.ones((input_ids.shape[0], max_length)  ,  dtype="i4")

							A    =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,  (input_ids.shape[0], input_ids.shape[-1] - 1)  ,  )
							A    =      model(
							    input_ids[:, :-1]  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  past_key_values=__SCREAMING_SNAKE_CASE  ,  position_ids=__SCREAMING_SNAKE_CASE  ,  )

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

							A    =      model(__SCREAMING_SNAKE_CASE)

							A    =      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				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    ,					A    =      (
							    inputs_dict["input_ids"],
							    inputs_dict["attention_mask"],
							)

							A    =      jnp.concatenate(
							    [
							        attention_mask,
							        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
							    ]  ,  axis=-1  ,  )

							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,  (input_ids.shape[0], input_ids.shape[-1] - 1)  ,  )

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

							A    =      model(__SCREAMING_SNAKE_CASE  ,  attention_mask=__SCREAMING_SNAKE_CASE)

							A    =      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				(     _A  ,	unittest.TestCase  ,	_A		):
		SCREAMING_SNAKE_CASE              =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		SCREAMING_SNAKE_CASE              =					() if is_flax_available() else ()


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      FlaxOPTModelTester(self)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    ,					A    =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    ,					A    =      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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							for model_class_name in self.all_model_classes:
												A    =      model_class_name.from_pretrained("facebook/opt-125m")
												A    =      np.ones((1, 1)) * model.config.eos_token_id
												A    =      model(__SCREAMING_SNAKE_CASE)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





@require_sentencepiece
@require_flax
class     __UpperCamelCase				(     unittest.TestCase		):


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    =      FlaxOPTModel.from_pretrained("facebook/opt-350m")
							A    =      jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
							A    =      model(input_ids=__SCREAMING_SNAKE_CASE).last_hidden_state
							A    =      (1, 1_1, 5_1_2)
							self.assertEqual(output.shape  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.array(
							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]])
							self.assertTrue(jnp.allclose(output[:, :3, :3]  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





@require_flax
@slow
class     __UpperCamelCase				(     unittest.TestCase		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							super().setUp()
							A    =      "facebook/opt-350m"


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      FlaxOPTForCausalLM.from_pretrained(self.path_model)
							A    =      GPTaTokenizer.from_pretrained(self.path_model)

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  add_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							A    =      jnp.array(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ])
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))

							A    =      jax.jit(__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





@require_flax
@slow
class     __UpperCamelCase				(     unittest.TestCase		):


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      "facebook/opt-125m"

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of New York, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							A    =      []

							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							A    =      "facebook/opt-350m"

							A    =      [
							    "Today is a beautiful day and I want to",
							    "In the city of San Francisco, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							A    =      []
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "facebook/opt-125m"
							A    =      [
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ]  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  )

							A    =      jax.jit(model.generate)

							A    =      jit_generate(inputs["input_ids"]  ,  attention_mask=inputs["attention_mask"]).sequences

							A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      "facebook/opt-350m"

							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

							A    =      "left"

							# use different length sentences to test batching
							A    =      [
							    "Hello, my dog is a little",
							    "Today, I",
							]

							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE)
							A    =      inputs["input_ids"]

							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  attention_mask=inputs["attention_mask"]  ,  trace=__SCREAMING_SNAKE_CASE)

							A    =      tokenizer(sentences[0]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE)

							A    =      inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
							A    =      tokenizer(sentences[1]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  max_length=model.config.max_length - num_paddings)

							A    =      tokenizer.batch_decode(outputs[0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_non_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)

							A    =      [
							    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
							    "Today, I was in the middle of a conversation with a friend about the",
							]
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  [non_padded_sentence, padded_sentence])





