

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=7 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=99 ,				__UpperCamelCase=32 ,				__UpperCamelCase=5 ,				__UpperCamelCase=4 ,				__UpperCamelCase=37 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=16 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=3 ,				__UpperCamelCase=4 ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= seq_length
				snake_case_      		= is_training
				snake_case_      		= use_input_mask
				snake_case_      		= use_token_type_ids
				snake_case_      		= use_labels
				snake_case_      		= vocab_size
				snake_case_      		= hidden_size
				snake_case_      		= num_hidden_layers
				snake_case_      		= num_attention_heads
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= max_position_embeddings
				snake_case_      		= type_vocab_size
				snake_case_      		= type_sequence_label_size
				snake_case_      		= initializer_range
				snake_case_      		= num_labels
				snake_case_      		= num_choices
				snake_case_      		= scope


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

				snake_case_      		= None
				if self.use_input_mask:
					snake_case_      		= random_attention_mask([self.batch_size, self.seq_length]							)

				snake_case_      		= None
				if self.use_token_type_ids:
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size							)

				snake_case_      		= None
				snake_case_      		= None
				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_choices							)

				snake_case_      		= self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return LlamaConfig(
				    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				is_decoder=__UpperCamelCase ,				initializer_range=self.initializer_range ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= LlamaModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= True
				snake_case_      		= LlamaModel(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				)
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				)
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= LlamaForCausalLM(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= True
				snake_case_      		= True
				snake_case_      		= LlamaForCausalLM(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()

				# first forward pass
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				use_cache=__UpperCamelCase ,				)
				snake_case_      		= outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				snake_case_      		= ids_tensor((self.batch_size, 3) ,				config.vocab_size							)
				snake_case_      		= ids_tensor((self.batch_size, 3) ,				vocab_size=2							)

				# append to next input_ids and
				snake_case_      		= torch.cat([input_ids, next_tokens] ,				dim=-1							)
				snake_case_      		= torch.cat([input_mask, next_mask] ,				dim=-1							)

				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				output_hidden_states=__UpperCamelCase ,				)['hidden_states'][0]
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				past_key_values=__UpperCamelCase ,				output_hidden_states=__UpperCamelCase ,				)['hidden_states'][0]

				# select random slice
				snake_case_      		= ids_tensor((1,) ,				output_from_past.shape[-1]							).item()
				snake_case_      		= output_from_no_past[:, -3:, random_slice_idx].detach()
				snake_case_      		= output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(__UpperCamelCase ,				__UpperCamelCase ,				atol=1E-3							)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				(
				    (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       
				)      		= config_and_inputs
				snake_case_      		= {'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			__A						  =  (LlamaForCausalLM,) if is_torch_available() else ()
			__A						  =  (
			    {
			        """feature-extraction""": LlamaModel,
			        """text-classification""": LlamaForSequenceClassification,
			        """text-generation""": LlamaForCausalLM,
			        """zero-shot""": LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LlamaModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					snake_case_      		= type
					self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= 3
				snake_case_      		= input_dict['input_ids']
				snake_case_      		= input_ids.ne(1							).to(__UpperCamelCase							)
				snake_case_      		= ids_tensor([self.model_tester.batch_size] ,				self.model_tester.type_sequence_label_size							)
				snake_case_      		= LlamaForSequenceClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.assertEqual(result.logits.shape ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= 3
				snake_case_      		= 'single_label_classification'
				snake_case_      		= input_dict['input_ids']
				snake_case_      		= input_ids.ne(1							).to(__UpperCamelCase							)
				snake_case_      		= ids_tensor([self.model_tester.batch_size] ,				self.model_tester.type_sequence_label_size							)
				snake_case_      		= LlamaForSequenceClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.assertEqual(result.logits.shape ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= 3
				snake_case_      		= 'multi_label_classification'
				snake_case_      		= input_dict['input_ids']
				snake_case_      		= input_ids.ne(1							).to(__UpperCamelCase							)
				snake_case_      		= ids_tensor(
				    [self.model_tester.batch_size, config.num_labels] ,				self.model_tester.type_sequence_label_size							).to(torch.float							)
				snake_case_      		= LlamaForSequenceClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.assertEqual(result.logits.shape ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


			@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass







			@parameterized.expand([('linear',), ('dynamic',)]							)
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= ids_tensor([1, 10] ,				config.vocab_size							)
				snake_case_      		= ids_tensor([1, int(config.max_position_embeddings * 1.5							)] ,				config.vocab_size							)

				set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
				snake_case_      		= LlamaModel(__UpperCamelCase							)
				original_model.to(__UpperCamelCase							)
				original_model.eval()
				snake_case_      		= original_model(__UpperCamelCase							).last_hidden_state
				snake_case_      		= original_model(__UpperCamelCase							).last_hidden_state

				set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
				snake_case_      		= {'type': scaling_type, 'factor': 10.0}
				snake_case_      		= LlamaModel(__UpperCamelCase							)
				scaled_model.to(__UpperCamelCase							)
				scaled_model.eval()
				snake_case_      		= scaled_model(__UpperCamelCase							).last_hidden_state
				snake_case_      		= scaled_model(__UpperCamelCase							).last_hidden_state

				# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
				# maximum sequence length, so the outputs for the short input should match.
				if scaling_type == "dynamic":
					self.assertTrue(torch.allclose(__UpperCamelCase ,				__UpperCamelCase ,				atol=1E-5							)							)
				else:
					self.assertFalse(torch.allclose(__UpperCamelCase ,				__UpperCamelCase ,				atol=1E-5							)							)

				# The output should be different for long inputs
				self.assertFalse(torch.allclose(__UpperCamelCase ,				__UpperCamelCase ,				atol=1E-5							)							)







@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
				snake_case_      		= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' ,				device_map='auto'							)
				snake_case_      		= model(torch.tensor([input_ids]							)							)
				# Expected mean on dim = -1
				snake_case_      		= torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]							)
				torch.testing.assert_close(out.mean(-1							) ,				__UpperCamelCase ,				atol=1E-2 ,				rtol=1E-2							)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				snake_case_      		= torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]							)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30] ,				__UpperCamelCase ,				atol=1E-5 ,				rtol=1E-5							)


			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
				snake_case_      		= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' ,				device_map='auto'							)
				snake_case_      		= model(torch.tensor(__UpperCamelCase							)							)
				# Expected mean on dim = -1
				snake_case_      		= torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]							)
				torch.testing.assert_close(out.mean(-1							) ,				__UpperCamelCase ,				atol=1E-2 ,				rtol=1E-2							)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				snake_case_      		= torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]							)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30] ,				__UpperCamelCase ,				atol=1E-5 ,				rtol=1E-5							)


			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
				snake_case_      		= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' ,				device_map='auto'							)
				snake_case_      		= model(torch.tensor(__UpperCamelCase							)							)
				# Expected mean on dim = -1
				snake_case_      		= torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]							)
				torch.testing.assert_close(out.mean(-1							) ,				__UpperCamelCase ,				atol=1E-2 ,				rtol=1E-2							)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				snake_case_      		= torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]							)
				# fmt: on
				torch.testing.assert_close(out.mean(-1							) ,				__UpperCamelCase ,				atol=1E-2 ,				rtol=1E-2							)


			@unittest.skip(
			    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
				snake_case_      		= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' ,				device_map='auto'							)
				snake_case_      		= model(torch.tensor(__UpperCamelCase							)							)

				snake_case_      		= torch.tensor(
				    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] ,				dtype=torch.floataa							)
				torch.testing.assert_close(out.mean(-1							) ,				__UpperCamelCase ,				atol=1E-2 ,				rtol=1E-2							)
				# fmt: off
				snake_case_      		= torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]							)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30] ,				__UpperCamelCase ,				atol=1E-5 ,				rtol=1E-5							)







			@unittest.skip('Model is curently gated'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
				snake_case_      		= 'Simply put, the theory of relativity states that '
				snake_case_      		= LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'							)
				snake_case_      		= tokenizer.encode(__UpperCamelCase ,				return_tensors='pt'							)
				snake_case_      		= LlamaForCausalLM.from_pretrained(
				    'meta-llama/Llama-2-13b-chat-hf' ,				device_map='sequential' ,				use_safetensors=__UpperCamelCase							)

				# greedy generation outputs
				snake_case_      		= model.generate(__UpperCamelCase ,				max_new_tokens=64 ,				top_p=__UpperCamelCase ,				temperature=1 ,				do_sample=__UpperCamelCase							)
				snake_case_      		= tokenizer.decode(generated_ids[0] ,				skip_special_tokens=__UpperCamelCase							)
				self.assertEqual(__UpperCamelCase ,				__UpperCamelCase							)



from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    AutoTokenizer,
		    TFAutoModelForCausalLM,
		    TFAutoModelForSeqaSeqLM,
		    TFAutoModelForSpeechSeqaSeq,
		    TFAutoModelForVisionaSeq,
		    TFBartForConditionalGeneration,
		    TFLogitsProcessorList,
		    TFMinLengthLogitsProcessor,
		    tf_top_k_top_p_filtering,
		)

if is_tensorflow_text_available():
		import tensorflow_text as text


@require_tf
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= tf.convert_to_tensor(
				    [
				        [
				            8.222_0991,  # 3rd highest value; idx. 0
				            -0.562_0044,
				            5.2322_9752,
				            4.038_6393,
				            -6.879_8378,
				            -0.5478_5802,
				            -3.201_2153,
				            2.9277_7176,
				            1.8817_1953,
				            7.3534_1276,  # 5th highest value; idx. 9
				            8.4320_7833,  # 2nd highest value; idx. 10
				            -9.8571_1836,
				            -5.9620_9236,
				            -1.1303_9161,
				            -7.111_5294,
				            -0.836_9633,
				            -5.318_6408,
				            7.0642_7407,
				            0.8136_9344,
				            -0.8202_3817,
				            -5.917_9796,
				            0.5881_3443,
				            -6.9977_8438,
				            4.7155_1189,
				            -0.1877_1637,
				            7.4402_0759,  # 4th highest value; idx. 25
				            9.3845_0987,  # 1st highest value; idx. 26
				            2.1266_2941,
				            -9.3256_2038,
				            2.3565_2522,
				        ],  # cummulative prob of 5 highest values <= 0.6
				        [
				            0.5842_5518,
				            4.5313_9238,
				            -5.5751_0464,
				            -6.2803_0699,
				            -7.1952_9503,
				            -4.0212_2551,
				            1.3933_7037,
				            -6.0670_7057,
				            1.5948_0517,
				            -9.64_3119,
				            0.0390_7799,
				            0.6723_1762,
				            -8.8820_6726,
				            6.2711_5922,  # 4th highest value; idx. 13
				            2.2852_0723,
				            4.8276_7506,
				            4.3042_1368,
				            8.827_5313,  # 2nd highest value; idx. 17
				            5.4402_9958,  # 5th highest value; idx. 18
				            -4.473_5794,
				            7.3857_9536,  # 3rd highest value; idx. 20
				            -2.9105_1663,
				            2.6194_6077,
				            -2.567_4762,
				            -9.4895_9302,
				            -4.0292_2645,
				            -1.3541_6918,
				            9.6770_2323,  # 1st highest value; idx. 27
				            -5.8947_8553,
				            1.8537_0467,
				        ],  # cummulative prob of 5 highest values <= 0.6
				    ] ,				dtype=tf.floataa ,				)

				snake_case_      		= tf.convert_to_tensor(
				    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,				dtype=tf.intaa ,				)  # expected non filtered idx as noted above

				snake_case_      		= tf.convert_to_tensor(
				    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] ,				dtype=tf.floataa ,				)  # expected non filtered values as noted above

				snake_case_      		= tf_top_k_top_p_filtering(__UpperCamelCase ,				top_k=10 ,				top_p=0.6 ,				min_tokens_to_keep=4							)

				snake_case_      		= output[output != -float('inf'							)]
				snake_case_      		= tf.cast(
				    tf.where(tf.not_equal(__UpperCamelCase ,				tf.constant(-float('inf'							) ,				dtype=tf.floataa							)							)							) ,				dtype=tf.intaa ,				)

				tf.debugging.assert_near(__UpperCamelCase ,				__UpperCamelCase ,				rtol=1E-12							)
				tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)







@require_tf
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							,						__snake_case							):






			"""simple docstring"""




			if is_tf_available():
						__A						  =  {
						    """AutoModelForCausalLM""": TFAutoModelForCausalLM,
						    """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
						    """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
						    """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
						    """LogitsProcessorList""": TFLogitsProcessorList,
						    """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
						    """create_tensor_fn""": tf.convert_to_tensor,
						    """floats_tensor""": floats_tensor,
						    """return_tensors""": """tf""",
						}


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 2
				snake_case_      		= 2


				class SCREAMING_SNAKE_CASE  (		tf.Module							):






							"""simple docstring"""




							def __init__(    self ,				__UpperCamelCase							):

								"""simple docstring"""


								super(__UpperCamelCase ,				self							).__init__()
								snake_case_      		= model







							@tf.function(
							    input_signature=(
							        tf.TensorSpec((None, input_length) ,				tf.intaa ,				name='input_ids'							),
							        tf.TensorSpec((None, input_length) ,				tf.intaa ,				name='attention_mask'							),
							    ) ,				jit_compile=__UpperCamelCase ,				)
							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

								"""simple docstring"""


								snake_case_      		= self.model.generate(
								    input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				max_new_tokens=__UpperCamelCase ,				return_dict_in_generate=__UpperCamelCase ,				)
								return {"sequences": outputs["sequences"]}







				snake_case_      		= [[2, 0], [1_02, 1_03]]
				snake_case_      		= [[1, 0], [1, 1]]
				snake_case_      		= DummyModel(model=__UpperCamelCase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(__UpperCamelCase ,				__UpperCamelCase ,				signatures={'serving_default': dummy_model.serving}							)
					snake_case_      		= tf.saved_model.load(__UpperCamelCase							).signatures['serving_default']
					for batch_size in range(1 ,				len(__UpperCamelCase							) + 1							):
						snake_case_      		= {
						    'input_ids': tf.constant(dummy_input_ids[:batch_size]							),
						    'attention_mask': tf.constant(dummy_attention_masks[:batch_size]							),
						}
						snake_case_      		= serving_func(**__UpperCamelCase							)['sequences']
						snake_case_      		= test_model.generate(**__UpperCamelCase ,				max_new_tokens=__UpperCamelCase							)
						tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 1
				snake_case_      		= 2


				class SCREAMING_SNAKE_CASE  (		tf.Module							):






							"""simple docstring"""




							def __init__(    self ,				__UpperCamelCase							):

								"""simple docstring"""


								super(__UpperCamelCase ,				self							).__init__()
								snake_case_      		= model







							@tf.function(
							    input_signature=(
							        tf.TensorSpec((batch_size, None) ,				tf.intaa ,				name='input_ids'							),
							        tf.TensorSpec((batch_size, None) ,				tf.intaa ,				name='attention_mask'							),
							    ) ,				jit_compile=__UpperCamelCase ,				)
							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

								"""simple docstring"""


								snake_case_      		= self.model.generate(
								    input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				max_new_tokens=__UpperCamelCase ,				return_dict_in_generate=__UpperCamelCase ,				)
								return {"sequences": outputs["sequences"]}







				snake_case_      		= [[2], [1_02, 1_03]]
				snake_case_      		= [[1], [1, 1]]
				snake_case_      		= DummyModel(model=__UpperCamelCase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(__UpperCamelCase ,				__UpperCamelCase ,				signatures={'serving_default': dummy_model.serving}							)
					snake_case_      		= tf.saved_model.load(__UpperCamelCase							).signatures['serving_default']
					for input_row in range(len(__UpperCamelCase							)							):
						snake_case_      		= {
						    'input_ids': tf.constant([dummy_input_ids[input_row]]							),
						    'attention_mask': tf.constant([dummy_attention_masks[input_row]]							),
						}
						snake_case_      		= serving_func(**__UpperCamelCase							)['sequences']
						snake_case_      		= test_model.generate(**__UpperCamelCase ,				max_new_tokens=__UpperCamelCase							)
						tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)


			@slow
			@require_tensorflow_text
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				with tempfile.TemporaryDirectory() as tmp_dir:
					# file needed to load the TF tokenizer
					hf_hub_download(repo_id='google/flan-t5-small' ,				filename='spiece.model' ,				local_dir=__UpperCamelCase							)


					class SCREAMING_SNAKE_CASE  (		tf.keras.layers.Layer							):






								"""simple docstring"""




								def __init__(    self							):

									"""simple docstring"""


									super().__init__()
									snake_case_      		= text.SentencepieceTokenizer(
									    model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase ,				'spiece.model'							) ,				'rb'							).read()							)
									snake_case_      		= TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5'							)







								def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				*__UpperCamelCase ,				**__UpperCamelCase							):

									"""simple docstring"""


									snake_case_      		= self.tokenizer.tokenize(__UpperCamelCase							)
									snake_case_		,       snake_case_      		= text.pad_model_inputs(
									    __UpperCamelCase ,				max_seq_length=64 ,				pad_value=self.model.config.pad_token_id							)
									snake_case_      		= self.model.generate(input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase							)
									return self.tokenizer.detokenize(__UpperCamelCase							)







					snake_case_      		= CompleteSentenceTransformer()
					snake_case_      		= tf.keras.layers.Input(shape=(1,) ,				dtype=tf.string ,				name='inputs'							)
					snake_case_      		= complete_model(__UpperCamelCase							)
					snake_case_      		= tf.keras.Model(__UpperCamelCase ,				__UpperCamelCase							)
					keras_model.save(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {
				    'do_sample': True,
				    'num_beams': 1,
				    'top_p': 0.7,
				    'top_k': 10,
				    'temperature': 0.7,
				}
				snake_case_      		= 14

				snake_case_      		= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 'Hello, my dog is cute and'
				snake_case_      		= tokenizer(__UpperCamelCase ,				return_tensors='tf'							)
				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)

				snake_case_      		= 6_38
				# forces the generation to happen on CPU, to avoid GPU-related quirks
				with tf.device(':/CPU:0'							):
					tf.random.set_seed(0							)
					snake_case_      		= model.generate(**__UpperCamelCase ,				eos_token_id=__UpperCamelCase ,				**__UpperCamelCase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)

				snake_case_      		= [6_38, 1_98]
				with tf.device(':/CPU:0'							):
					tf.random.set_seed(0							)
					snake_case_      		= model.generate(**__UpperCamelCase ,				eos_token_id=__UpperCamelCase ,				**__UpperCamelCase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= 'Hugging Face is a technology company based in New York and Paris.'
				snake_case_      		= bart_tokenizer(__UpperCamelCase ,				return_tensors='tf'							).input_ids
				snake_case_      		= TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= bart_model.generate(__UpperCamelCase							).numpy()


				class SCREAMING_SNAKE_CASE  (		__snake_case							):






							"""simple docstring"""




							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

								"""simple docstring"""


								return super().call(__UpperCamelCase ,				**__UpperCamelCase							)







				snake_case_      		= FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= bart_model.generate(__UpperCamelCase ,				foo='bar'							).numpy()
				self.assertTrue(np.array_equal(__UpperCamelCase ,				__UpperCamelCase							)							)


				class SCREAMING_SNAKE_CASE  (		bart_model.model.encoder.__class__							):






							"""simple docstring"""




							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

								"""simple docstring"""


								return super().call(__UpperCamelCase ,				**__UpperCamelCase							)







				snake_case_      		= FakeEncoder(bart_model.config ,				bart_model.model.shared							)
				snake_case_      		= fake_encoder

				# Normal generation still works (the output will be different because the encoder weights are different)
				snake_case_      		= bart_model.generate(__UpperCamelCase							).numpy()
				with self.assertRaises(__UpperCamelCase							):
					# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
					bart_model.generate(__UpperCamelCase ,				foo='bar'							)
