




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_ 		=  "platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    if attention_mask is None:
        __UpperCamelCase			=    np.where(input_ids != config.pad_token_id       ,  1       ,  0   )
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }

@require_flax
class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Tuple			,							snake_case       :			Optional[Any]			,							snake_case       :			Union[str, Any]=13			,							snake_case       :			int=7			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			Any=False			,							snake_case       :			List[str]=99			,							snake_case       :			Union[str, Any]=16			,							snake_case       :			Union[str, Any]=2			,							snake_case       :			Any=4			,							snake_case       :			int=4			,							snake_case       :			str="gelu"			,							snake_case       :			Optional[int]=0.1			,							snake_case       :			int=0.1			,							snake_case       :			Any=20			,							snake_case       :			int=2			,							snake_case       :			Optional[Any]=1			,							snake_case       :			int=0			,							snake_case       :			str=16			,							snake_case       :			Any=16			,							snake_case       :			Optional[Any]=0.02			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    seq_length
          __UpperCamelCase			=    is_training
          __UpperCamelCase			=    use_labels
          __UpperCamelCase			=    vocab_size
          __UpperCamelCase			=    hidden_size
          __UpperCamelCase			=    num_hidden_layers
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    intermediate_size
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    max_position_embeddings
          __UpperCamelCase			=    eos_token_id
          __UpperCamelCase			=    pad_token_id
          __UpperCamelCase			=    bos_token_id
          __UpperCamelCase			=    embed_dim
          __UpperCamelCase			=    word_embed_proj_dim
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    False







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

          __UpperCamelCase			=    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=snake_case			,							word_embed_proj_dim=self.word_embed_proj_dim			,							initializer_range=self.initializer_range			,							use_cache=snake_case			,							)
          __UpperCamelCase			=    prepare_opt_inputs_dict(snake_case			,							snake_case					)
          return config, inputs_dict







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







      def    snake_case						(  self       :			Tuple			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			Any					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase			=    inputs_dict['''input_ids''']
          __UpperCamelCase			=    inputs_dict['''attention_mask''']

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.ones((input_ids.shape[0], max_length)			,							dtype='''i4'''					)

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

          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							attention_mask=snake_case			,							past_key_values=outputs_cache.past_key_values			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case					)

          __UpperCamelCase			=    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    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Any			,							snake_case       :			int					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase		,   __UpperCamelCase			=    (
              inputs_dict['''input_ids'''],
              inputs_dict['''attention_mask'''],
          )

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

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

          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)
          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							past_key_values=outputs_cache.past_key_values			,							attention_mask=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case			,							attention_mask=snake_case					)

          __UpperCamelCase			=    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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Any     =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__      :			Optional[int]     =       () if is_flax_available() else ()







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTModelTester(self					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    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    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    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					)







      @slow
      def    snake_case						(  self       :			Union[str, Any]					):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase			=    model_class_name.from_pretrained('''facebook/opt-125m'''					)
              __UpperCamelCase			=    np.ones((1, 1)					) * model.config.eos_token_id
              __UpperCamelCase			=    model(snake_case					)
              self.assertIsNotNone(snake_case					)







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

      """simple docstring"""

      @slow
      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
          __UpperCamelCase			=    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
          __UpperCamelCase			=    model(input_ids=snake_case					).last_hidden_state
          __UpperCamelCase			=    (1, 11, 512)
          self.assertEqual(output.shape			,							snake_case					)
          __UpperCamelCase			=    jnp.array(
              [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]					)
          self.assertTrue(jnp.allclose(output[:, :3, :3]			,							snake_case			,							atol=4E-2					)					)







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

      """simple docstring"""

      def    snake_case						(  self       :			Optional[Any]					):
          super().setUp()
          __UpperCamelCase			=    '''facebook/opt-350m'''







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(self.path_model					)

          __UpperCamelCase			=    [
              '''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
          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case			,							add_special_tokens=snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          __UpperCamelCase			=    jnp.array(
              [
                  [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                  [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                  [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                  [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
              ]					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)

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







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

      """simple docstring"""

      @property
      def    snake_case						(  self       :			Any					):
          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    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''

          __UpperCamelCase			=    [
              '''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''',
          ]

          __UpperCamelCase			=    []

          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    [
              '''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''',
          ]

          __UpperCamelCase			=    []
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







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

          __UpperCamelCase			=    jax.jit(model.generate					)

          __UpperCamelCase			=    jit_generate(inputs['''input_ids''']			,							attention_mask=inputs['''attention_mask''']					).sequences

          __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)

          self.assertIsNotNone(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)

          __UpperCamelCase			=    '''left'''

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

          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case					)
          __UpperCamelCase			=    inputs['''input_ids''']

          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							attention_mask=inputs['''attention_mask''']			,							trace=snake_case					)

          __UpperCamelCase			=    tokenizer(sentences[0]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case					)

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

          __UpperCamelCase			=    tokenizer.batch_decode(outputs[0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_non_padded[0][0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_padded[0][0]			,							skip_special_tokens=snake_case					)

          __UpperCamelCase			=    [
              '''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(snake_case			,							snake_case					)
          self.assertListEqual(snake_case			,							[non_padded_sentence, padded_sentence]					)









"""simple docstring"""



from __future__ import annotations





def 			_snake_case  (     UpperCAmelCase_	:       list[list[int]]       , UpperCAmelCase_	:       list[list[int]]      ):
     assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

     A__				      =	len(UpperCAmelCase_      )
     A__				      =	list(range(UpperCAmelCase_      )      )
     A__				      =	[-1] * n  # who the donor has donated to
     A__				      =	[-1] * n  # who the recipient has received from
     A__				      =	[0] * n

     while unmatched_donors:
          A__				      =	unmatched_donors[0]
          A__				      =	donor_pref[donor]
          A__				      =	donor_preference[num_donations[donor]]
          num_donations[donor] += 1
          A__				      =	recipient_pref[recipient]
          A__				      =	rec_record[recipient]

          if prev_donor != -1:
               if rec_preference.index(UpperCAmelCase_      ) > rec_preference.index(UpperCAmelCase_      ):
                    A__				      =	donor
                    A__				      =	recipient
                    unmatched_donors.append(UpperCAmelCase_      )
                    unmatched_donors.remove(UpperCAmelCase_      )
          else:
               A__				      =	donor
               A__				      =	recipient
               unmatched_donors.remove(UpperCAmelCase_      )
     return donor_record






