




'''simple docstring'''



import math
import unittest

from transformers import BioGptConfig, is_torch_available
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 (
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptTokenizer,
      )
      from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST






class 				_a							:





       '''simple docstring'''






       def __init__(	self,				A,				A=13,				A=7,				A=True,				A=True,				A=False,				A=True,				A=99,				A=32,				A=5,				A=4,				A=37,				A="gelu",				A=0.1,				A=0.1,				A=512,				A=16,				A=2,				A=0.02,				A=3,				A=4,				A=None,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		parent
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		batch_size
             SCREAMING_SNAKE_CASE					:	int   =		seq_length
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		is_training
             SCREAMING_SNAKE_CASE					:	Any   =		use_input_mask
             SCREAMING_SNAKE_CASE					:	Any   =		use_token_type_ids
             SCREAMING_SNAKE_CASE					:	str   =		use_labels
             SCREAMING_SNAKE_CASE					:	int   =		vocab_size
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		hidden_size
             SCREAMING_SNAKE_CASE					:	str   =		num_hidden_layers
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		num_attention_heads
             SCREAMING_SNAKE_CASE					:	Dict   =		intermediate_size
             SCREAMING_SNAKE_CASE					:	Tuple   =		hidden_act
             SCREAMING_SNAKE_CASE					:	Dict   =		hidden_dropout_prob
             SCREAMING_SNAKE_CASE					:	str   =		attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		max_position_embeddings
             SCREAMING_SNAKE_CASE					:	Dict   =		type_vocab_size
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		type_sequence_label_size
             SCREAMING_SNAKE_CASE					:	Tuple   =		initializer_range
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		num_labels
             SCREAMING_SNAKE_CASE					:	Any   =		num_choices
             SCREAMING_SNAKE_CASE					:	Dict   =		scope







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length],				self.vocab_size   )

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		None
             if self.use_input_mask:
                   SCREAMING_SNAKE_CASE					:	Dict   =		random_attention_mask([self.batch_size, self.seq_length]   )

             SCREAMING_SNAKE_CASE					:	str   =		None
             if self.use_token_type_ids:
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length],				self.type_vocab_size   )

             SCREAMING_SNAKE_CASE					:	List[str]   =		None
             SCREAMING_SNAKE_CASE					:	Any   =		None
             SCREAMING_SNAKE_CASE					:	str   =		None
             if self.use_labels:
                   SCREAMING_SNAKE_CASE					:	int   =		ids_tensor([self.batch_size],				self.type_sequence_label_size   )
                   SCREAMING_SNAKE_CASE					:	Any   =		ids_tensor([self.batch_size, self.seq_length],				self.num_labels   )
                   SCREAMING_SNAKE_CASE					:	str   =		ids_tensor([self.batch_size],				self.num_choices   )

             SCREAMING_SNAKE_CASE					:	str   =		self.get_config()

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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return BioGptConfig(
                 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=__lowercase,				initializer_range=self.initializer_range,				)







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				A,				A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		BioGptModel(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             SCREAMING_SNAKE_CASE					:	int   =		model(__lowercase,				attention_mask=__lowercase   )
             SCREAMING_SNAKE_CASE					:	int   =		model(__lowercase   )
             self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				A,				A,				A,				A,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		BioGptForCausalLM(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()
             SCREAMING_SNAKE_CASE					:	Any   =		model(__lowercase,				attention_mask=__lowercase,				token_type_ids=__lowercase,				labels=__lowercase   )
             self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				*A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		BioGptModel(config=__lowercase   )
             model.to(__lowercase   )
             model.eval()

             # create attention mask
             SCREAMING_SNAKE_CASE					:	Tuple   =		torch.ones(input_ids.shape,				dtype=torch.long,				device=__lowercase   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.seq_length // 2
             SCREAMING_SNAKE_CASE					:	Tuple   =		0

             # first forward pass
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		model(__lowercase,				attention_mask=__lowercase   ).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             SCREAMING_SNAKE_CASE					:	List[Any]   =		ids_tensor((self.batch_size, 1),				config.vocab_size   )

             # change a random masked slice from input_ids
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		ids_tensor((1,),				__lowercase   ).item() + 1
             SCREAMING_SNAKE_CASE					:	str   =		ids_tensor((self.batch_size, 1),				config.vocab_size   ).squeeze(-1   )
             SCREAMING_SNAKE_CASE					:	int   =		random_other_next_tokens

             # append to next input_ids and attn_mask
             SCREAMING_SNAKE_CASE					:	Tuple   =		torch.cat([input_ids, next_tokens],				dim=-1   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1),				dtype=torch.long,				device=__lowercase   )],				dim=1,				)

             # get two different outputs
             SCREAMING_SNAKE_CASE					:	List[str]   =		model(__lowercase,				attention_mask=__lowercase   )['''last_hidden_state''']
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		model(__lowercase,				past_key_values=__lowercase,				attention_mask=__lowercase   )['''last_hidden_state''']

             # select random slice
             SCREAMING_SNAKE_CASE					:	str   =		ids_tensor((1,),				output_from_past.shape[-1]   ).item()
             SCREAMING_SNAKE_CASE					:	str   =		output_from_no_past[:, -1, random_slice_idx].detach()
             SCREAMING_SNAKE_CASE					:	Tuple   =		output_from_past[:, 0, random_slice_idx].detach()

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







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				*A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		BioGptModel(config=__lowercase   ).to(__lowercase   ).eval()

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		torch.ones(input_ids.shape,				dtype=torch.long,				device=__lowercase   )

             # first forward pass
             SCREAMING_SNAKE_CASE					:	Dict   =		model(__lowercase,				attention_mask=__lowercase,				use_cache=__lowercase   )

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		outputs.to_tuple()

             # create hypothetical multiple next token and extent to next_input_ids
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		ids_tensor((self.batch_size, 3),				config.vocab_size   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		ids_tensor((self.batch_size, 3),				2   )

             # append to next input_ids and
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		torch.cat([input_ids, next_tokens],				dim=-1   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.cat([attention_mask, next_attn_mask],				dim=-1   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		model(__lowercase,				attention_mask=__lowercase   )['''last_hidden_state''']
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		model(__lowercase,				attention_mask=__lowercase,				past_key_values=__lowercase   )[
                 '''last_hidden_state'''
             ]

             # select random slice
             SCREAMING_SNAKE_CASE					:	Tuple   =		ids_tensor((1,),				output_from_past.shape[-1]   ).item()
             SCREAMING_SNAKE_CASE					:	int   =		output_from_no_past[:, -3:, random_slice_idx].detach()
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		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(__lowercase,				__lowercase,				atol=1E-3   )   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				*A,				A=False   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		BioGptForCausalLM(__lowercase   )
             model.to(__lowercase   )
             if gradient_checkpointing:
                   model.gradient_checkpointing_enable()

             SCREAMING_SNAKE_CASE					:	str   =		model(__lowercase,				labels=__lowercase   )
             self.parent.assertEqual(result.loss.shape,				()   )
             self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)   )
             result.loss.backward()







       def     UpperCamelCase_				(	self,				A,				*A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		BioGptModel(__lowercase   )
             SCREAMING_SNAKE_CASE					:	int   =		model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers   )
             for key in model.state_dict().keys():
                   if "c_proj" in key and "weight" in key:
                         self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]   ) - model_std   ),				0.0_01   )
                         self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]   ) - 0.0   ),				0.01   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A,				A,				*A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		self.num_labels
             SCREAMING_SNAKE_CASE					:	List[Any]   =		BioGptForTokenClassification(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             SCREAMING_SNAKE_CASE					:	List[str]   =		model(__lowercase,				attention_mask=__lowercase,				token_type_ids=__lowercase   )
             self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels)   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.prepare_config_and_inputs()
             (
                 SCREAMING_SNAKE_CASE       
             )					:	Union[str, Any]   =		config_and_inputs
             SCREAMING_SNAKE_CASE					:	Any   =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class 				_a							(       UpperCAmelCase_      ,	UpperCAmelCase_      ,	UpperCAmelCase_      ,	unittest.TestCase      ):





       '''simple docstring'''






       A		:							Optional[int]								=		(
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       A		:							str								=		(BioGptForCausalLM,) if is_torch_available() else ()
       A		:							str								=		(
           {
               """feature-extraction""": BioGptModel,
               """text-classification""": BioGptForSequenceClassification,
               """text-generation""": BioGptForCausalLM,
               """token-classification""": BioGptForTokenClassification,
               """zero-shot""": BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       A		:							Any								=		False







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		BioGptModelTester(self   )
             SCREAMING_SNAKE_CASE					:	List[Any]   =		ConfigTester(self,				config_class=__lowercase,				hidden_size=37   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		type
                   self.model_tester.create_and_check_model(*__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*__lowercase,				gradient_checkpointing=__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*__lowercase   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		BioGptForCausalLM.from_pretrained('microsoft/biogpt'   )
             model.to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Dict   =		BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		'''left'''

             # Define PAD Token = EOS Token = 50256
             SCREAMING_SNAKE_CASE					:	List[str]   =		tokenizer.eos_token
             SCREAMING_SNAKE_CASE					:	Dict   =		model.config.eos_token_id

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

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		tokenizer(__lowercase,				return_tensors='pt',				padding=__lowercase   )
             SCREAMING_SNAKE_CASE					:	str   =		inputs['''input_ids'''].to(__lowercase   )

             SCREAMING_SNAKE_CASE					:	Any   =		model.generate(
                 input_ids=__lowercase,				attention_mask=inputs['attention_mask'].to(__lowercase   ),				)

             SCREAMING_SNAKE_CASE					:	str   =		tokenizer(sentences[0],				return_tensors='pt'   ).input_ids.to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model.generate(input_ids=__lowercase   )

             SCREAMING_SNAKE_CASE					:	Dict   =		inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
             SCREAMING_SNAKE_CASE					:	List[Any]   =		tokenizer(sentences[1],				return_tensors='pt'   ).input_ids.to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model.generate(input_ids=__lowercase,				max_length=model.config.max_length - num_paddings   )

             SCREAMING_SNAKE_CASE					:	str   =		tokenizer.batch_decode(__lowercase,				skip_special_tokens=__lowercase   )
             SCREAMING_SNAKE_CASE					:	str   =		tokenizer.decode(output_non_padded[0],				skip_special_tokens=__lowercase   )
             SCREAMING_SNAKE_CASE					:	int   =		tokenizer.decode(output_padded[0],				skip_special_tokens=__lowercase   )

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		[
                 '''Hello, my dog is a little bit bigger than a little bit.''',
                 '''Today, I have a good idea of how to use the information''',
             ]
             self.assertListEqual(__lowercase,				__lowercase   )
             self.assertListEqual(__lowercase,				[non_padded_sentence, padded_sentence]   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		BioGptModel.from_pretrained(__lowercase   )
                   self.assertIsNotNone(__lowercase   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE					:	List[str]   =		3
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		input_dict['''input_ids''']
             SCREAMING_SNAKE_CASE					:	Dict   =		input_ids.ne(1   ).to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Dict   =		ids_tensor([self.model_tester.batch_size],				self.model_tester.type_sequence_label_size   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		BioGptForSequenceClassification(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             SCREAMING_SNAKE_CASE					:	Dict   =		model(__lowercase,				attention_mask=__lowercase,				labels=__lowercase   )
             self.assertEqual(result.logits.shape,				(self.model_tester.batch_size, self.model_tester.num_labels)   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE					:	Tuple   =		3
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		'''multi_label_classification'''
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		input_dict['''input_ids''']
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		input_ids.ne(1   ).to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		ids_tensor(
                 [self.model_tester.batch_size, config.num_labels],				self.model_tester.type_sequence_label_size   ).to(torch.float   )
             SCREAMING_SNAKE_CASE					:	Tuple   =		BioGptForSequenceClassification(__lowercase   )
             model.to(__lowercase   )
             model.eval()
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model(__lowercase,				attention_mask=__lowercase,				labels=__lowercase   )
             self.assertEqual(result.logits.shape,				(self.model_tester.batch_size, self.model_tester.num_labels)   )






@require_torch
class 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		BioGptForCausalLM.from_pretrained('microsoft/biogpt'   )
             SCREAMING_SNAKE_CASE					:	str   =		torch.tensor([[2, 4_805, 9, 656, 21]]   )
             SCREAMING_SNAKE_CASE					:	int   =		model(__lowercase   )[0]

             SCREAMING_SNAKE_CASE					:	List[str]   =		42_384

             SCREAMING_SNAKE_CASE					:	str   =		torch.Size((1, 5, vocab_size)   )
             self.assertEqual(output.shape,				__lowercase   )

             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor(
                 [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]   )

             self.assertTrue(torch.allclose(output[:, :3, :3],				__lowercase,				atol=1E-4   )   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		BioGptTokenizer.from_pretrained('microsoft/biogpt'   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		BioGptForCausalLM.from_pretrained('microsoft/biogpt'   )
             model.to(__lowercase   )

             torch.manual_seed(0   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		tokenizer('COVID-19 is',				return_tensors='pt'   ).to(__lowercase   )
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		model.generate(
                 **__lowercase,				min_length=100,				max_length=1_024,				num_beams=5,				early_stopping=__lowercase,				)
             SCREAMING_SNAKE_CASE					:	List[str]   =		tokenizer.decode(output_ids[0],				skip_special_tokens=__lowercase   )

             SCREAMING_SNAKE_CASE					:	str   =		(
                 '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
                 ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
                 ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
                 ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
                 ''' more than 800,000 deaths.'''
             )
             self.assertEqual(__lowercase,				__lowercase   )



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		R'''\w+[.]\d+'''
    __UpperCamelCase     :List[str]			  =		re.findall(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for pat in pats:
        __UpperCamelCase     :int			  =		key.replace(SCREAMING_SNAKE_CASE  ,			'''_'''.join(pat.split('''.'''	)	)	)
    return key



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key	)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        __UpperCamelCase     :Any			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        __UpperCamelCase     :List[Any]			  =		pt_tensor.transpose(2  ,			3  ,			1  ,			0	)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        __UpperCamelCase     :Any			  =		pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=42	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    __UpperCamelCase     :str			  =		flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE	)	)

    __UpperCamelCase     :int			  =		flatten_dict(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        __UpperCamelCase     :List[Any]			  =		rename_key(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :List[Any]			  =		tuple(renamed_pt_key.split('''.'''	)	)

        # Correctly rename weight parameters
        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
        __UpperCamelCase     :str			  =		jnp.asarray(SCREAMING_SNAKE_CASE	)

    return unflatten_dict(SCREAMING_SNAKE_CASE	)





