


from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    Dict						,      snake_case	:    Any=None						,      snake_case	:    Union[str, Any]=None )       ->							Tuple:



   if attention_mask is None:
      __lowerCamelCase:				Optional[int]     =	tf.cast(tf.math.not_equal(snake_case						,      config.pad_token_id )						,      tf.inta )
   return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class        a					:
     UpperCAmelCase__ :  Optional[int]						      =	OPTConfig
     UpperCAmelCase__ :  Optional[Any]						      =	{}
     UpperCAmelCase__ :  Tuple						      =	"gelu"





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				int     =	ids_tensor([self.batch_size, self.seq_length - 1]    ,	self.vocab_size							)
        __lowerCamelCase:				Union[str, Any]     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,	1							)
        __lowerCamelCase:				List[str]     =	tf.concat([input_ids, eos_tensor]    ,	axis=1							)

        __lowerCamelCase:				str     =	self.config_cls(
            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    ,	word_embed_proj_dim=self.word_embed_proj_dim    ,	is_encoder_decoder=snake_case__    ,	**self.config_updates    ,	)
        __lowerCamelCase:				Optional[Any]     =	prepare_opt_inputs_dict(snake_case__    ,	snake_case__							)
        return config, inputs_dict







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				Union[str, Any]     =	TFOPTModel(config=snake_case__							)
        __lowerCamelCase:				Any     =	inputs_dict["input_ids"]

        __lowerCamelCase:				Optional[int]     =	input_ids[:1, :]
        __lowerCamelCase:				Dict     =	inputs_dict["attention_mask"][:1, :]
        __lowerCamelCase:				int     =	1

        # first forward pass
        __lowerCamelCase:				int     =	model(snake_case__    ,	attention_mask=snake_case__    ,	use_cache=snake_case__							)

        __lowerCamelCase:				str     =	outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        __lowerCamelCase:				int     =	ids_tensor((self.batch_size, 3)    ,	config.vocab_size							)
        __lowerCamelCase:				Optional[int]     =	tf.cast(ids_tensor((self.batch_size, 3)    ,	2							)    ,	tf.inta							)

        # append to next input_ids and
        __lowerCamelCase:				List[Any]     =	tf.concat([input_ids, next_tokens]    ,	axis=-1							)
        __lowerCamelCase:				List[Any]     =	tf.concat([attention_mask, next_attn_mask]    ,	axis=-1							)

        __lowerCamelCase:				List[str]     =	model(snake_case__    ,	attention_mask=snake_case__							)[0]
        __lowerCamelCase:				Union[str, Any]     =	model(snake_case__    ,	attention_mask=snake_case__    ,	past_key_values=snake_case__							)[0]

        self.parent.assertEqual(next_tokens.shape[1]    ,	output_from_past.shape[1]							)

        # select random slice
        __lowerCamelCase:				List[Any]     =	int(ids_tensor((1,)    ,	output_from_past.shape[-1]							)							)
        __lowerCamelCase:				Any     =	output_from_no_past[:, -3:, random_slice_idx]
        __lowerCamelCase:				Any     =	output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(snake_case__    ,	snake_case__    ,	rtol=1E-3							)



@require_tf
class        a					(			SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(TFOPTForCausalLM,) if is_tf_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
     )
     UpperCAmelCase__ :  Dict						      =	False
     UpperCAmelCase__ :  List[Any]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  int						      =	10





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	TFOPTModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(self    ,	config_class=snake_case__							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*snake_case__							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()

        def _get_word_embedding_weight(SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
           if hasattr(snake_case__    ,	"""weight"""							):
              return embedding_layer.weight
           else:
              # Here we build the word embeddings weights if not exists.
              # And then we retry to get the attribute once built.
              model.build()
              if hasattr(snake_case__    ,	"""weight"""							):
                 return embedding_layer.weight
              else:
                 return None

        for model_class in self.all_model_classes:
           for size in [config.vocab_size - 10, config.vocab_size + 10]:
              # build the embeddings
              __lowerCamelCase:				List[Any]     =	model_class(config=snake_case__							)
              __lowerCamelCase:				Optional[int]     =	_get_word_embedding_weight(snake_case__    ,	model.get_input_embeddings()							)
              __lowerCamelCase:				Union[str, Any]     =	_get_word_embedding_weight(snake_case__    ,	model.get_output_embeddings()							)

              # reshape the embeddings
              model.resize_token_embeddings(snake_case__							)
              __lowerCamelCase:				List[str]     =	_get_word_embedding_weight(snake_case__    ,	model.get_input_embeddings()							)
              __lowerCamelCase:				List[Any]     =	_get_word_embedding_weight(snake_case__    ,	model.get_output_embeddings()							)

              # check that the resized embeddings size matches the desired size.
              __lowerCamelCase:				str     =	size if size is not None else config.vocab_size

              self.assertEqual(new_input_embeddings.shape[0]    ,	snake_case__							)

              # check that weights remain the same after resizing
              __lowerCamelCase:				Any     =	True
              for pa, pa in zip(old_input_embeddings.value()    ,	new_input_embeddings.value()							):
                 if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
                    __lowerCamelCase:				Dict     =	False
              self.assertTrue(snake_case__							)

              if old_output_embeddings is not None and new_output_embeddings is not None:
                 self.assertEqual(new_output_embeddings.shape[0]    ,	snake_case__							)

                 __lowerCamelCase:				List[str]     =	True
                 for pa, pa in zip(old_output_embeddings.value()    ,	new_output_embeddings.value()							):
                    if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
                       __lowerCamelCase:				str     =	False
                 self.assertTrue(snake_case__							)



def     __lowerCAmelCase						(							snake_case	:    str )       ->							int:
   return tf.constant(snake_case						,      dtype=tf.intaa )



@require_tf
class        a					(			unittest.TestCase   ):
     UpperCAmelCase__ :  int						      =	99





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				str     =	tf.ones((4, 1)    ,	dtype=tf.intaa							) * 2
        __lowerCamelCase:				Dict     =	tf.concat([ids_tensor((4, 6)    ,	self.vocab_size - 3							) + 3, eos_column_vector]    ,	axis=1							)
        __lowerCamelCase:				str     =	input_ids.shape[0]
        __lowerCamelCase:				Optional[int]     =	OPTConfig(
            vocab_size=self.vocab_size    ,	hidden_size=24    ,	num_hidden_layers=2    ,	num_attention_heads=2    ,	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



@require_sentencepiece
@require_tf
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	TFOPTModel.from_pretrained("""facebook/opt-350m"""							)
        __lowerCamelCase:				str     =	_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
        __lowerCamelCase:				Tuple     =	tf.not_equal(snake_case__    ,	model.config.pad_token_id							)
        with tf.GradientTape():
           __lowerCamelCase:				Tuple     =	model(input_ids=snake_case__    ,	attention_mask=snake_case__							).last_hidden_state
        __lowerCamelCase:				List[str]     =	(1, 11, 512)
        self.assertEqual(output.shape    ,	snake_case__							)
        __lowerCamelCase:				Dict     =	tf.constant(
            [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]							)
        self.assertTrue(np.allclose(output[:, :3, :3]    ,	snake_case__    ,	atol=4E-3							)							)

        __lowerCamelCase:				int     =	tf.function(snake_case__    ,	jit_compile=snake_case__							)
        __lowerCamelCase:				List[str]     =	xla_generate(snake_case__    ,	snake_case__							)[0]
        self.assertTrue(np.allclose(output[:, :3, :3]    ,	snake_case__    ,	atol=4E-2							)							)



@require_tf
@slow
class        a					(			unittest.TestCase   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        super().setUp()
        __lowerCamelCase:				List[str]     =	"facebook/opt-350m"







     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Optional[int]     =	TFOPTForCausalLM.from_pretrained(self.path_model							)
        __lowerCamelCase:				str     =	GPTaTokenizer.from_pretrained(self.path_model							)

        __lowerCamelCase:				Tuple     =	[
            "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
        __lowerCamelCase:				int     =	tokenizer(snake_case__    ,	return_tensors="""tf"""    ,	padding=snake_case__    ,	add_special_tokens=snake_case__							)
        __lowerCamelCase:				int     =	tf.math.reduce_mean(model(inputs.input_ids    ,	attention_mask=inputs.attention_mask							)[0]    ,	axis=-1							)
        __lowerCamelCase:				int     =	tf.constant(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]							)
        self.assertTrue(np.allclose(snake_case__    ,	snake_case__    ,	atol=1E-4							)							)

        __lowerCamelCase:				int     =	tf.function(snake_case__    ,	jit_compile=snake_case__							)
        __lowerCamelCase:				Optional[Any]     =	tf.math.reduce_mean(xla_generate(inputs.input_ids    ,	attention_mask=inputs.attention_mask							)[0]    ,	axis=-1							)
        self.assertTrue(np.allclose(snake_case__    ,	snake_case__    ,	atol=1E-4							)							)



@require_tf
@slow
class        a					(			unittest.TestCase   ):





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        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					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	"facebook/opt-125m"

        __lowerCamelCase:				List[str]     =	[
            "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",
        ]

        __lowerCamelCase:				Union[str, Any]     =	[]
        __lowerCamelCase:				Optional[Any]     =	GPTaTokenizer.from_pretrained(snake_case__							)
        __lowerCamelCase:				Any     =	TFOPTForCausalLM.from_pretrained(snake_case__							)

        for prompt in self.prompts:
           __lowerCamelCase:				Optional[int]     =	tokenizer(snake_case__    ,	return_tensors="""tf"""							).input_ids

           __lowerCamelCase:				List[str]     =	model.generate(snake_case__    ,	max_length=10							)

           __lowerCamelCase:				Union[str, Any]     =	tokenizer.batch_decode(snake_case__    ,	skip_special_tokens=snake_case__							)
           predicted_outputs += generated_string

        self.assertListEqual(snake_case__    ,	snake_case__							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				List[str]     =	"facebook/opt-350m"

        __lowerCamelCase:				Optional[Any]     =	GPTaTokenizer.from_pretrained(snake_case__							)
        __lowerCamelCase:				Optional[int]     =	TFOPTForCausalLM.from_pretrained(snake_case__							)

        __lowerCamelCase:				List[str]     =	"left"

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

        __lowerCamelCase:				Optional[int]     =	tokenizer(snake_case__    ,	return_tensors="""tf"""    ,	padding=snake_case__							)
        __lowerCamelCase:				int     =	inputs["input_ids"]

        __lowerCamelCase:				int     =	model.generate(input_ids=snake_case__    ,	attention_mask=inputs["""attention_mask"""]							)

        __lowerCamelCase:				Dict     =	tokenizer(sentences[0]    ,	return_tensors="""tf"""							).input_ids
        __lowerCamelCase:				Optional[Any]     =	model.generate(input_ids=snake_case__							)

        __lowerCamelCase:				Tuple     =	inputs_non_padded.shape[-1] - tf.math.reduce_sum(
            tf.cast(inputs["""attention_mask"""][-1]    ,	tf.intaa							)							)
        __lowerCamelCase:				Optional[Any]     =	tokenizer(sentences[1]    ,	return_tensors="""tf"""							).input_ids
        __lowerCamelCase:				Union[str, Any]     =	model.generate(input_ids=snake_case__    ,	max_length=model.config.max_length - num_paddings							)

        __lowerCamelCase:				Tuple     =	tokenizer.batch_decode(snake_case__    ,	skip_special_tokens=snake_case__							)
        __lowerCamelCase:				Optional[Any]     =	tokenizer.decode(output_non_padded[0]    ,	skip_special_tokens=snake_case__							)
        __lowerCamelCase:				int     =	tokenizer.decode(output_padded[0]    ,	skip_special_tokens=snake_case__							)

        __lowerCamelCase:				List[Any]     =	[
            "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]							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[str]     =	"facebook/opt-350m"

        __lowerCamelCase:				Optional[int]     =	[
            "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",
        ]

        __lowerCamelCase:				Dict     =	[]
        __lowerCamelCase:				Optional[int]     =	GPTaTokenizer.from_pretrained(snake_case__							)
        __lowerCamelCase:				int     =	TFOPTForCausalLM.from_pretrained(snake_case__							)

        for prompt in self.prompts:
           __lowerCamelCase:				Dict     =	tokenizer(snake_case__    ,	return_tensors="""tf"""							).input_ids

           __lowerCamelCase:				Any     =	model.generate(snake_case__    ,	max_length=10							)

           __lowerCamelCase:				Tuple     =	tokenizer.batch_decode(snake_case__    ,	skip_special_tokens=snake_case__							)
           predicted_outputs += generated_string

        self.assertListEqual(snake_case__    ,	snake_case__							)







from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


